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R Software Module

rwasp_multipleregression.wasp

Title produced by software

Multiple Regression

Date of computation

Mon, 05 Dec 2016 16:06:09 +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/05/t14809508519emdlnx8q1n89nh.htm/, Retrieved Wed, 01 May 2024 18:28:10 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=297739, Retrieved Wed, 01 May 2024 18:28:10 +0000

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No (this computation is public)

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Estimated Impact

110

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

-       [Multiple Regression] [] [2016-12-05 15:06:09] [df90c754990be6fd2b18fcd529010a59] [Current]

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14	4	3	3	3
19	5	4	4	3
17	4	5	5	3
17	NA	4	4	3
15	NA	4	4	4
20	5	3	5	3
15	5	3	5	NA
19	NA	4	5	3
15	NA	4	5	4
15	5	4	5	3
19	5	4	5	3
NA	4	4	4	3
20	4	4	4	4
18	4	3	4	3
15	4	4	4	4
14	5	4	5	3
20	NA	4	4	4
NA	NA	NA	NA	NA
16	3	4	4	NA
16	NA	4	5	4
16	5	4	4	3
10	NA	4	4	3
19	5	4	4	3
19	NA	4	4	3
16	NA	4	5	4
15	NA	3	5	3
18	4	4	4	4
17	4	4	4	3
19	4	4	5	3
17	4	4	5	3
NA	3	4	3	3
19	4	3	5	3
20	5	4	4	4
5	NA	4	5	2
19	4	2	4	3
16	5	4	5	3
15	NA	4	4	3
16	3	3	4	4
18	2	4	4	4
16	5	4	5	4
15	NA	4	4	3
17	5	4	5	3
NA	4	3	3	3
20	4	4	5	3
19	4	4	4	3
7	3	4	5	3
13	NA	4	5	3
16	4	4	4	3
16	3	4	3	3
NA	NA	3	NA	NA
18	5	4	5	3
18	NA	5	5	3
16	NA	5	4	4
17	2	3	3	3
19	3	4	4	3
16	2	4	4	3
19	NA	4	4	3
13	5	5	4	3
16	4	4	4	4
13	NA	4	4	3
12	5	4	5	3
17	5	4	4	3
17	4	5	4	3
17	5	4	4	3
16	4	4	4	3
16	4	2	4	2
14	5	4	5	3
16	3	4	4	3
13	2	4	4	4
16	5	4	4	3
14	NA	4	4	3
20	NA	4	4	3
12	NA	4	3	3
13	NA	3	4	3
18	NA	5	4	4
14	4	4	4	3
19	5	3	5	3
18	3	4	4	3
14	2	4	4	5
18	5	4	5	3
19	NA	4	5	3
15	1	3	3	3
14	NA	4	5	3
17	5	4	4	4
19	NA	4	5	4
13	5	5	5	5
19	4	4	5	4
18	5	4	5	4
20	NA	4	4	3
15	5	4	4	4
15	5	4	2	3
15	NA	4	4	3
20	4	5	5	3
15	NA	4	5	3
19	4	5	5	3
18	NA	4	4	3
18	4	4	4	4
15	4	5	4	5
20	5	4	5	4
17	5	4	4	3
12	NA	4	NA	NA
18	NA	4	5	4
19	4	4	4	3
20	2	4	4	3
NA	NA	4	4	3
17	NA	4	5	4
15	NA	4	4	4
16	NA	4	5	3
18	NA	4	4	3
18	4	4	4	4
14	NA	4	4	4
15	NA	4	3	3
12	NA	4	4	3
17	3	3	3	3
14	5	4	5	NA
18	4	4	4	4
17	5	4	4	3
17	NA	4	5	4
20	5	4	4	3
16	3	4	4	3
14	4	4	4	3
15	3	4	4	3
18	NA	4	4	4
20	4	4	4	3
17	NA	4	5	4
17	4	4	4	3
17	5	4	4	3
17	NA	4	5	3
15	NA	4	4	3
17	NA	4	4	3
18	2	3	3	3
17	4	4	4	NA
20	4	5	4	5
15	NA	3	4	3
16	2	3	3	3
15	NA	4	4	NA
18	4	4	5	5
11	NA	NA	3	NA
15	4	4	4	3
18	5	5	5	4
20	4	5	5	3
19	NA	3	4	3
14	3	4	4	3
16	4	4	4	NA
15	3	4	3	3
17	4	5	5	3
18	2	4	4	4
20	5	5	5	4
17	4	3	4	3
18	NA	4	4	4
15	NA	3	3	3
16	4	4	4	4
11	5	4	4	3
15	4	4	4	3
18	2	4	3	3
17	NA	4	4	3
16	5	4	5	3
12	NA	4	3	3
19	NA	4	4	3
18	5	4	5	4
15	4	4	4	3
17	5	5	5	3
19	3	4	4	4
18	NA	4	4	3
19	4	NA	4	4
16	NA	3	4	3
16	4	4	4	4
16	NA	4	3	3
14	3	4	4	5

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	7 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 time7 seconds \tabularnewline
R ServerBig Analytics Cloud Computing Center \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=297739&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]

7 seconds[/C][/ROW] [ROW]

R Server[/C]

Big Analytics Cloud Computing Center[/C][/ROW] [/TABLE] Source: https://freestatistics.org/blog/index.php?pk=297739&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=297739&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	7 seconds
R Server	Big Analytics Cloud Computing Center

Multiple Linear Regression - Estimated Regression Equation
ITHSUM[t] = + 14.928 + 0.0932036Retour[t] -0.257259Kwaliteit[t] + 0.510287Tevredenheid[t] + 0.117075Verwachtingen[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
ITHSUM[t] =  +  14.928 +  0.0932036Retour[t] -0.257259Kwaliteit[t] +  0.510287Tevredenheid[t] +  0.117075Verwachtingen[t]  + e[t] \tabularnewline
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=297739&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]ITHSUM[t] =  +  14.928 +  0.0932036Retour[t] -0.257259Kwaliteit[t] +  0.510287Tevredenheid[t] +  0.117075Verwachtingen[t]  + e[t][/C][/ROW]
[ROW][C][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=297739&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=297739&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] = + 14.928 + 0.0932036Retour[t] -0.257259Kwaliteit[t] + 0.510287Tevredenheid[t] + 0.117075Verwachtingen[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)	+14.93	2.104	+7.0930e+00	2.324e-10	1.162e-10
Retour	+0.0932	0.2706	+3.4450e-01	0.7313	0.3656
Kwaliteit	-0.2573	0.4478	-5.7450e-01	0.567	0.2835
Tevredenheid	+0.5103	0.4305	+1.1850e+00	0.2388	0.1194
Verwachtingen	+0.1171	0.4002	+2.9250e-01	0.7705	0.3853

\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) & +14.93 &  2.104 & +7.0930e+00 &  2.324e-10 &  1.162e-10 \tabularnewline
Retour & +0.0932 &  0.2706 & +3.4450e-01 &  0.7313 &  0.3656 \tabularnewline
Kwaliteit & -0.2573 &  0.4478 & -5.7450e-01 &  0.567 &  0.2835 \tabularnewline
Tevredenheid & +0.5103 &  0.4305 & +1.1850e+00 &  0.2388 &  0.1194 \tabularnewline
Verwachtingen & +0.1171 &  0.4002 & +2.9250e-01 &  0.7705 &  0.3853 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=297739&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]+14.93[/C][C] 2.104[/C][C]+7.0930e+00[/C][C] 2.324e-10[/C][C] 1.162e-10[/C][/ROW]
[ROW][C]Retour[/C][C]+0.0932[/C][C] 0.2706[/C][C]+3.4450e-01[/C][C] 0.7313[/C][C] 0.3656[/C][/ROW]
[ROW][C]Kwaliteit[/C][C]-0.2573[/C][C] 0.4478[/C][C]-5.7450e-01[/C][C] 0.567[/C][C] 0.2835[/C][/ROW]
[ROW][C]Tevredenheid[/C][C]+0.5103[/C][C] 0.4305[/C][C]+1.1850e+00[/C][C] 0.2388[/C][C] 0.1194[/C][/ROW]
[ROW][C]Verwachtingen[/C][C]+0.1171[/C][C] 0.4002[/C][C]+2.9250e-01[/C][C] 0.7705[/C][C] 0.3853[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=297739&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=297739&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)	+14.93	2.104	+7.0930e+00	2.324e-10	1.162e-10
Retour	+0.0932	0.2706	+3.4450e-01	0.7313	0.3656
Kwaliteit	-0.2573	0.4478	-5.7450e-01	0.567	0.2835
Tevredenheid	+0.5103	0.4305	+1.1850e+00	0.2388	0.1194
Verwachtingen	+0.1171	0.4002	+2.9250e-01	0.7705	0.3853

Multiple Linear Regression - Regression Statistics
Multiple R	0.1579
R-squared	0.02492
Adjusted R-squared	-0.01613
F-TEST (value)	0.607
F-TEST (DF numerator)	4
F-TEST (DF denominator)	95
p-value	0.6585
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	2.299
Sum Squared Residuals	501.9

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R &  0.1579 \tabularnewline
R-squared &  0.02492 \tabularnewline
Adjusted R-squared & -0.01613 \tabularnewline
F-TEST (value) &  0.607 \tabularnewline
F-TEST (DF numerator) & 4 \tabularnewline
F-TEST (DF denominator) & 95 \tabularnewline
p-value &  0.6585 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation &  2.299 \tabularnewline
Sum Squared Residuals &  501.9 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=297739&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C] 0.1579[/C][/ROW]
[ROW][C]R-squared[/C][C] 0.02492[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]-0.01613[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C] 0.607[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]4[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]95[/C][/ROW]
[ROW][C]p-value[/C][C] 0.6585[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C] 2.299[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C] 501.9[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=297739&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=297739&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	0.1579
R-squared	0.02492
Adjusted R-squared	-0.01613
F-TEST (value)	0.607
F-TEST (DF numerator)	4
F-TEST (DF denominator)	95
p-value	0.6585
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	2.299
Sum Squared Residuals	501.9

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	InterpolationForecast	ResidualsPrediction Error
1	14	16.41	-2.411
2	19	16.76	2.243
3	17	16.92	0.08283
4	20	17.52	2.475
5	15	17.27	-2.268
6	19	17.27	1.732
7	20	16.78	3.219
8	18	16.92	1.079
9	15	16.78	-1.781
10	14	17.27	-3.268
11	16	16.76	-0.7573
12	19	16.76	2.243
13	18	16.78	1.219
14	17	16.66	0.3359
15	19	17.17	1.826
16	17	17.17	-0.1744
17	19	17.43	1.568
18	20	16.87	3.126
19	19	17.18	1.821
20	16	17.27	-1.268
21	16	16.95	-0.9453
22	18	16.59	1.405
23	16	17.38	-1.385
24	17	17.27	-0.2676
25	20	17.17	2.826
26	19	16.66	2.336
27	7	17.08	-10.08
28	16	16.66	-0.6641
29	16	16.06	-0.06066
30	18	17.27	0.7324
31	17	16.22	0.7753
32	19	16.57	2.429
33	16	16.48	-0.4777
34	13	16.5	-3.5
35	16	16.78	-0.7812
36	12	17.27	-5.268
37	17	16.76	0.2427
38	17	16.41	0.5931
39	17	16.76	0.2427
40	16	16.66	-0.6641
41	16	17.06	-1.062
42	14	17.27	-3.268
43	16	16.57	-0.5709
44	13	16.59	-3.595
45	16	16.76	-0.7573
46	14	16.66	-2.664
47	19	17.52	1.475
48	18	16.57	1.429
49	14	16.71	-2.712
50	18	17.27	0.7324
51	15	16.13	-1.132
52	17	16.87	0.1256
53	13	17.24	-4.245
54	19	17.29	1.708
55	18	17.38	0.6153
56	15	16.87	-1.874
57	15	15.74	-0.7368
58	20	16.92	3.083
59	19	16.92	2.083
60	18	16.78	1.219
61	15	16.64	-1.641
62	20	17.38	2.615
63	17	16.76	0.2427
64	19	16.66	2.336
65	20	16.48	3.522
66	18	16.78	1.219
67	17	16.32	0.6821
68	18	16.78	1.219
69	17	16.76	0.2427
70	20	16.76	3.243
71	16	16.57	-0.5709
72	14	16.66	-2.664
73	15	16.57	-1.571
74	20	16.66	3.336
75	17	16.66	0.3359
76	17	16.76	0.2427
77	18	16.22	1.775
78	20	16.64	3.359
79	16	16.22	-0.2247
80	18	17.41	0.5914
81	15	16.66	-1.664
82	18	17.13	0.8725
83	20	16.92	3.083
84	14	16.57	-2.571
85	15	16.06	-1.061
86	17	16.92	0.08283
87	18	16.59	1.405
88	20	17.13	2.873
89	17	16.92	0.0786
90	16	16.78	-0.7812
91	11	16.76	-5.757
92	15	16.66	-1.664
93	18	15.97	2.033
94	16	17.27	-1.268
95	18	17.38	0.6153
96	15	16.66	-1.664
97	17	17.01	-0.01038
98	19	16.69	2.312
99	16	16.78	-0.7812
100	14	16.81	-2.805

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 &  14 &  16.41 & -2.411 \tabularnewline
2 &  19 &  16.76 &  2.243 \tabularnewline
3 &  17 &  16.92 &  0.08283 \tabularnewline
4 &  20 &  17.52 &  2.475 \tabularnewline
5 &  15 &  17.27 & -2.268 \tabularnewline
6 &  19 &  17.27 &  1.732 \tabularnewline
7 &  20 &  16.78 &  3.219 \tabularnewline
8 &  18 &  16.92 &  1.079 \tabularnewline
9 &  15 &  16.78 & -1.781 \tabularnewline
10 &  14 &  17.27 & -3.268 \tabularnewline
11 &  16 &  16.76 & -0.7573 \tabularnewline
12 &  19 &  16.76 &  2.243 \tabularnewline
13 &  18 &  16.78 &  1.219 \tabularnewline
14 &  17 &  16.66 &  0.3359 \tabularnewline
15 &  19 &  17.17 &  1.826 \tabularnewline
16 &  17 &  17.17 & -0.1744 \tabularnewline
17 &  19 &  17.43 &  1.568 \tabularnewline
18 &  20 &  16.87 &  3.126 \tabularnewline
19 &  19 &  17.18 &  1.821 \tabularnewline
20 &  16 &  17.27 & -1.268 \tabularnewline
21 &  16 &  16.95 & -0.9453 \tabularnewline
22 &  18 &  16.59 &  1.405 \tabularnewline
23 &  16 &  17.38 & -1.385 \tabularnewline
24 &  17 &  17.27 & -0.2676 \tabularnewline
25 &  20 &  17.17 &  2.826 \tabularnewline
26 &  19 &  16.66 &  2.336 \tabularnewline
27 &  7 &  17.08 & -10.08 \tabularnewline
28 &  16 &  16.66 & -0.6641 \tabularnewline
29 &  16 &  16.06 & -0.06066 \tabularnewline
30 &  18 &  17.27 &  0.7324 \tabularnewline
31 &  17 &  16.22 &  0.7753 \tabularnewline
32 &  19 &  16.57 &  2.429 \tabularnewline
33 &  16 &  16.48 & -0.4777 \tabularnewline
34 &  13 &  16.5 & -3.5 \tabularnewline
35 &  16 &  16.78 & -0.7812 \tabularnewline
36 &  12 &  17.27 & -5.268 \tabularnewline
37 &  17 &  16.76 &  0.2427 \tabularnewline
38 &  17 &  16.41 &  0.5931 \tabularnewline
39 &  17 &  16.76 &  0.2427 \tabularnewline
40 &  16 &  16.66 & -0.6641 \tabularnewline
41 &  16 &  17.06 & -1.062 \tabularnewline
42 &  14 &  17.27 & -3.268 \tabularnewline
43 &  16 &  16.57 & -0.5709 \tabularnewline
44 &  13 &  16.59 & -3.595 \tabularnewline
45 &  16 &  16.76 & -0.7573 \tabularnewline
46 &  14 &  16.66 & -2.664 \tabularnewline
47 &  19 &  17.52 &  1.475 \tabularnewline
48 &  18 &  16.57 &  1.429 \tabularnewline
49 &  14 &  16.71 & -2.712 \tabularnewline
50 &  18 &  17.27 &  0.7324 \tabularnewline
51 &  15 &  16.13 & -1.132 \tabularnewline
52 &  17 &  16.87 &  0.1256 \tabularnewline
53 &  13 &  17.24 & -4.245 \tabularnewline
54 &  19 &  17.29 &  1.708 \tabularnewline
55 &  18 &  17.38 &  0.6153 \tabularnewline
56 &  15 &  16.87 & -1.874 \tabularnewline
57 &  15 &  15.74 & -0.7368 \tabularnewline
58 &  20 &  16.92 &  3.083 \tabularnewline
59 &  19 &  16.92 &  2.083 \tabularnewline
60 &  18 &  16.78 &  1.219 \tabularnewline
61 &  15 &  16.64 & -1.641 \tabularnewline
62 &  20 &  17.38 &  2.615 \tabularnewline
63 &  17 &  16.76 &  0.2427 \tabularnewline
64 &  19 &  16.66 &  2.336 \tabularnewline
65 &  20 &  16.48 &  3.522 \tabularnewline
66 &  18 &  16.78 &  1.219 \tabularnewline
67 &  17 &  16.32 &  0.6821 \tabularnewline
68 &  18 &  16.78 &  1.219 \tabularnewline
69 &  17 &  16.76 &  0.2427 \tabularnewline
70 &  20 &  16.76 &  3.243 \tabularnewline
71 &  16 &  16.57 & -0.5709 \tabularnewline
72 &  14 &  16.66 & -2.664 \tabularnewline
73 &  15 &  16.57 & -1.571 \tabularnewline
74 &  20 &  16.66 &  3.336 \tabularnewline
75 &  17 &  16.66 &  0.3359 \tabularnewline
76 &  17 &  16.76 &  0.2427 \tabularnewline
77 &  18 &  16.22 &  1.775 \tabularnewline
78 &  20 &  16.64 &  3.359 \tabularnewline
79 &  16 &  16.22 & -0.2247 \tabularnewline
80 &  18 &  17.41 &  0.5914 \tabularnewline
81 &  15 &  16.66 & -1.664 \tabularnewline
82 &  18 &  17.13 &  0.8725 \tabularnewline
83 &  20 &  16.92 &  3.083 \tabularnewline
84 &  14 &  16.57 & -2.571 \tabularnewline
85 &  15 &  16.06 & -1.061 \tabularnewline
86 &  17 &  16.92 &  0.08283 \tabularnewline
87 &  18 &  16.59 &  1.405 \tabularnewline
88 &  20 &  17.13 &  2.873 \tabularnewline
89 &  17 &  16.92 &  0.0786 \tabularnewline
90 &  16 &  16.78 & -0.7812 \tabularnewline
91 &  11 &  16.76 & -5.757 \tabularnewline
92 &  15 &  16.66 & -1.664 \tabularnewline
93 &  18 &  15.97 &  2.033 \tabularnewline
94 &  16 &  17.27 & -1.268 \tabularnewline
95 &  18 &  17.38 &  0.6153 \tabularnewline
96 &  15 &  16.66 & -1.664 \tabularnewline
97 &  17 &  17.01 & -0.01038 \tabularnewline
98 &  19 &  16.69 &  2.312 \tabularnewline
99 &  16 &  16.78 & -0.7812 \tabularnewline
100 &  14 &  16.81 & -2.805 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=297739&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] 14[/C][C] 16.41[/C][C]-2.411[/C][/ROW]
[ROW][C]2[/C][C] 19[/C][C] 16.76[/C][C] 2.243[/C][/ROW]
[ROW][C]3[/C][C] 17[/C][C] 16.92[/C][C] 0.08283[/C][/ROW]
[ROW][C]4[/C][C] 20[/C][C] 17.52[/C][C] 2.475[/C][/ROW]
[ROW][C]5[/C][C] 15[/C][C] 17.27[/C][C]-2.268[/C][/ROW]
[ROW][C]6[/C][C] 19[/C][C] 17.27[/C][C] 1.732[/C][/ROW]
[ROW][C]7[/C][C] 20[/C][C] 16.78[/C][C] 3.219[/C][/ROW]
[ROW][C]8[/C][C] 18[/C][C] 16.92[/C][C] 1.079[/C][/ROW]
[ROW][C]9[/C][C] 15[/C][C] 16.78[/C][C]-1.781[/C][/ROW]
[ROW][C]10[/C][C] 14[/C][C] 17.27[/C][C]-3.268[/C][/ROW]
[ROW][C]11[/C][C] 16[/C][C] 16.76[/C][C]-0.7573[/C][/ROW]
[ROW][C]12[/C][C] 19[/C][C] 16.76[/C][C] 2.243[/C][/ROW]
[ROW][C]13[/C][C] 18[/C][C] 16.78[/C][C] 1.219[/C][/ROW]
[ROW][C]14[/C][C] 17[/C][C] 16.66[/C][C] 0.3359[/C][/ROW]
[ROW][C]15[/C][C] 19[/C][C] 17.17[/C][C] 1.826[/C][/ROW]
[ROW][C]16[/C][C] 17[/C][C] 17.17[/C][C]-0.1744[/C][/ROW]
[ROW][C]17[/C][C] 19[/C][C] 17.43[/C][C] 1.568[/C][/ROW]
[ROW][C]18[/C][C] 20[/C][C] 16.87[/C][C] 3.126[/C][/ROW]
[ROW][C]19[/C][C] 19[/C][C] 17.18[/C][C] 1.821[/C][/ROW]
[ROW][C]20[/C][C] 16[/C][C] 17.27[/C][C]-1.268[/C][/ROW]
[ROW][C]21[/C][C] 16[/C][C] 16.95[/C][C]-0.9453[/C][/ROW]
[ROW][C]22[/C][C] 18[/C][C] 16.59[/C][C] 1.405[/C][/ROW]
[ROW][C]23[/C][C] 16[/C][C] 17.38[/C][C]-1.385[/C][/ROW]
[ROW][C]24[/C][C] 17[/C][C] 17.27[/C][C]-0.2676[/C][/ROW]
[ROW][C]25[/C][C] 20[/C][C] 17.17[/C][C] 2.826[/C][/ROW]
[ROW][C]26[/C][C] 19[/C][C] 16.66[/C][C] 2.336[/C][/ROW]
[ROW][C]27[/C][C] 7[/C][C] 17.08[/C][C]-10.08[/C][/ROW]
[ROW][C]28[/C][C] 16[/C][C] 16.66[/C][C]-0.6641[/C][/ROW]
[ROW][C]29[/C][C] 16[/C][C] 16.06[/C][C]-0.06066[/C][/ROW]
[ROW][C]30[/C][C] 18[/C][C] 17.27[/C][C] 0.7324[/C][/ROW]
[ROW][C]31[/C][C] 17[/C][C] 16.22[/C][C] 0.7753[/C][/ROW]
[ROW][C]32[/C][C] 19[/C][C] 16.57[/C][C] 2.429[/C][/ROW]
[ROW][C]33[/C][C] 16[/C][C] 16.48[/C][C]-0.4777[/C][/ROW]
[ROW][C]34[/C][C] 13[/C][C] 16.5[/C][C]-3.5[/C][/ROW]
[ROW][C]35[/C][C] 16[/C][C] 16.78[/C][C]-0.7812[/C][/ROW]
[ROW][C]36[/C][C] 12[/C][C] 17.27[/C][C]-5.268[/C][/ROW]
[ROW][C]37[/C][C] 17[/C][C] 16.76[/C][C] 0.2427[/C][/ROW]
[ROW][C]38[/C][C] 17[/C][C] 16.41[/C][C] 0.5931[/C][/ROW]
[ROW][C]39[/C][C] 17[/C][C] 16.76[/C][C] 0.2427[/C][/ROW]
[ROW][C]40[/C][C] 16[/C][C] 16.66[/C][C]-0.6641[/C][/ROW]
[ROW][C]41[/C][C] 16[/C][C] 17.06[/C][C]-1.062[/C][/ROW]
[ROW][C]42[/C][C] 14[/C][C] 17.27[/C][C]-3.268[/C][/ROW]
[ROW][C]43[/C][C] 16[/C][C] 16.57[/C][C]-0.5709[/C][/ROW]
[ROW][C]44[/C][C] 13[/C][C] 16.59[/C][C]-3.595[/C][/ROW]
[ROW][C]45[/C][C] 16[/C][C] 16.76[/C][C]-0.7573[/C][/ROW]
[ROW][C]46[/C][C] 14[/C][C] 16.66[/C][C]-2.664[/C][/ROW]
[ROW][C]47[/C][C] 19[/C][C] 17.52[/C][C] 1.475[/C][/ROW]
[ROW][C]48[/C][C] 18[/C][C] 16.57[/C][C] 1.429[/C][/ROW]
[ROW][C]49[/C][C] 14[/C][C] 16.71[/C][C]-2.712[/C][/ROW]
[ROW][C]50[/C][C] 18[/C][C] 17.27[/C][C] 0.7324[/C][/ROW]
[ROW][C]51[/C][C] 15[/C][C] 16.13[/C][C]-1.132[/C][/ROW]
[ROW][C]52[/C][C] 17[/C][C] 16.87[/C][C] 0.1256[/C][/ROW]
[ROW][C]53[/C][C] 13[/C][C] 17.24[/C][C]-4.245[/C][/ROW]
[ROW][C]54[/C][C] 19[/C][C] 17.29[/C][C] 1.708[/C][/ROW]
[ROW][C]55[/C][C] 18[/C][C] 17.38[/C][C] 0.6153[/C][/ROW]
[ROW][C]56[/C][C] 15[/C][C] 16.87[/C][C]-1.874[/C][/ROW]
[ROW][C]57[/C][C] 15[/C][C] 15.74[/C][C]-0.7368[/C][/ROW]
[ROW][C]58[/C][C] 20[/C][C] 16.92[/C][C] 3.083[/C][/ROW]
[ROW][C]59[/C][C] 19[/C][C] 16.92[/C][C] 2.083[/C][/ROW]
[ROW][C]60[/C][C] 18[/C][C] 16.78[/C][C] 1.219[/C][/ROW]
[ROW][C]61[/C][C] 15[/C][C] 16.64[/C][C]-1.641[/C][/ROW]
[ROW][C]62[/C][C] 20[/C][C] 17.38[/C][C] 2.615[/C][/ROW]
[ROW][C]63[/C][C] 17[/C][C] 16.76[/C][C] 0.2427[/C][/ROW]
[ROW][C]64[/C][C] 19[/C][C] 16.66[/C][C] 2.336[/C][/ROW]
[ROW][C]65[/C][C] 20[/C][C] 16.48[/C][C] 3.522[/C][/ROW]
[ROW][C]66[/C][C] 18[/C][C] 16.78[/C][C] 1.219[/C][/ROW]
[ROW][C]67[/C][C] 17[/C][C] 16.32[/C][C] 0.6821[/C][/ROW]
[ROW][C]68[/C][C] 18[/C][C] 16.78[/C][C] 1.219[/C][/ROW]
[ROW][C]69[/C][C] 17[/C][C] 16.76[/C][C] 0.2427[/C][/ROW]
[ROW][C]70[/C][C] 20[/C][C] 16.76[/C][C] 3.243[/C][/ROW]
[ROW][C]71[/C][C] 16[/C][C] 16.57[/C][C]-0.5709[/C][/ROW]
[ROW][C]72[/C][C] 14[/C][C] 16.66[/C][C]-2.664[/C][/ROW]
[ROW][C]73[/C][C] 15[/C][C] 16.57[/C][C]-1.571[/C][/ROW]
[ROW][C]74[/C][C] 20[/C][C] 16.66[/C][C] 3.336[/C][/ROW]
[ROW][C]75[/C][C] 17[/C][C] 16.66[/C][C] 0.3359[/C][/ROW]
[ROW][C]76[/C][C] 17[/C][C] 16.76[/C][C] 0.2427[/C][/ROW]
[ROW][C]77[/C][C] 18[/C][C] 16.22[/C][C] 1.775[/C][/ROW]
[ROW][C]78[/C][C] 20[/C][C] 16.64[/C][C] 3.359[/C][/ROW]
[ROW][C]79[/C][C] 16[/C][C] 16.22[/C][C]-0.2247[/C][/ROW]
[ROW][C]80[/C][C] 18[/C][C] 17.41[/C][C] 0.5914[/C][/ROW]
[ROW][C]81[/C][C] 15[/C][C] 16.66[/C][C]-1.664[/C][/ROW]
[ROW][C]82[/C][C] 18[/C][C] 17.13[/C][C] 0.8725[/C][/ROW]
[ROW][C]83[/C][C] 20[/C][C] 16.92[/C][C] 3.083[/C][/ROW]
[ROW][C]84[/C][C] 14[/C][C] 16.57[/C][C]-2.571[/C][/ROW]
[ROW][C]85[/C][C] 15[/C][C] 16.06[/C][C]-1.061[/C][/ROW]
[ROW][C]86[/C][C] 17[/C][C] 16.92[/C][C] 0.08283[/C][/ROW]
[ROW][C]87[/C][C] 18[/C][C] 16.59[/C][C] 1.405[/C][/ROW]
[ROW][C]88[/C][C] 20[/C][C] 17.13[/C][C] 2.873[/C][/ROW]
[ROW][C]89[/C][C] 17[/C][C] 16.92[/C][C] 0.0786[/C][/ROW]
[ROW][C]90[/C][C] 16[/C][C] 16.78[/C][C]-0.7812[/C][/ROW]
[ROW][C]91[/C][C] 11[/C][C] 16.76[/C][C]-5.757[/C][/ROW]
[ROW][C]92[/C][C] 15[/C][C] 16.66[/C][C]-1.664[/C][/ROW]
[ROW][C]93[/C][C] 18[/C][C] 15.97[/C][C] 2.033[/C][/ROW]
[ROW][C]94[/C][C] 16[/C][C] 17.27[/C][C]-1.268[/C][/ROW]
[ROW][C]95[/C][C] 18[/C][C] 17.38[/C][C] 0.6153[/C][/ROW]
[ROW][C]96[/C][C] 15[/C][C] 16.66[/C][C]-1.664[/C][/ROW]
[ROW][C]97[/C][C] 17[/C][C] 17.01[/C][C]-0.01038[/C][/ROW]
[ROW][C]98[/C][C] 19[/C][C] 16.69[/C][C] 2.312[/C][/ROW]
[ROW][C]99[/C][C] 16[/C][C] 16.78[/C][C]-0.7812[/C][/ROW]
[ROW][C]100[/C][C] 14[/C][C] 16.81[/C][C]-2.805[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=297739&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=297739&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	14	16.41	-2.411
2	19	16.76	2.243
3	17	16.92	0.08283
4	20	17.52	2.475
5	15	17.27	-2.268
6	19	17.27	1.732
7	20	16.78	3.219
8	18	16.92	1.079
9	15	16.78	-1.781
10	14	17.27	-3.268
11	16	16.76	-0.7573
12	19	16.76	2.243
13	18	16.78	1.219
14	17	16.66	0.3359
15	19	17.17	1.826
16	17	17.17	-0.1744
17	19	17.43	1.568
18	20	16.87	3.126
19	19	17.18	1.821
20	16	17.27	-1.268
21	16	16.95	-0.9453
22	18	16.59	1.405
23	16	17.38	-1.385
24	17	17.27	-0.2676
25	20	17.17	2.826
26	19	16.66	2.336
27	7	17.08	-10.08
28	16	16.66	-0.6641
29	16	16.06	-0.06066
30	18	17.27	0.7324
31	17	16.22	0.7753
32	19	16.57	2.429
33	16	16.48	-0.4777
34	13	16.5	-3.5
35	16	16.78	-0.7812
36	12	17.27	-5.268
37	17	16.76	0.2427
38	17	16.41	0.5931
39	17	16.76	0.2427
40	16	16.66	-0.6641
41	16	17.06	-1.062
42	14	17.27	-3.268
43	16	16.57	-0.5709
44	13	16.59	-3.595
45	16	16.76	-0.7573
46	14	16.66	-2.664
47	19	17.52	1.475
48	18	16.57	1.429
49	14	16.71	-2.712
50	18	17.27	0.7324
51	15	16.13	-1.132
52	17	16.87	0.1256
53	13	17.24	-4.245
54	19	17.29	1.708
55	18	17.38	0.6153
56	15	16.87	-1.874
57	15	15.74	-0.7368
58	20	16.92	3.083
59	19	16.92	2.083
60	18	16.78	1.219
61	15	16.64	-1.641
62	20	17.38	2.615
63	17	16.76	0.2427
64	19	16.66	2.336
65	20	16.48	3.522
66	18	16.78	1.219
67	17	16.32	0.6821
68	18	16.78	1.219
69	17	16.76	0.2427
70	20	16.76	3.243
71	16	16.57	-0.5709
72	14	16.66	-2.664
73	15	16.57	-1.571
74	20	16.66	3.336
75	17	16.66	0.3359
76	17	16.76	0.2427
77	18	16.22	1.775
78	20	16.64	3.359
79	16	16.22	-0.2247
80	18	17.41	0.5914
81	15	16.66	-1.664
82	18	17.13	0.8725
83	20	16.92	3.083
84	14	16.57	-2.571
85	15	16.06	-1.061
86	17	16.92	0.08283
87	18	16.59	1.405
88	20	17.13	2.873
89	17	16.92	0.0786
90	16	16.78	-0.7812
91	11	16.76	-5.757
92	15	16.66	-1.664
93	18	15.97	2.033
94	16	17.27	-1.268
95	18	17.38	0.6153
96	15	16.66	-1.664
97	17	17.01	-0.01038
98	19	16.69	2.312
99	16	16.78	-0.7812
100	14	16.81	-2.805

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
8	0.7051	0.5898	0.2949
9	0.7962	0.4076	0.2038
10	0.8774	0.2452	0.1226
11	0.8057	0.3886	0.1943
12	0.7913	0.4174	0.2087
13	0.7106	0.5788	0.2894
14	0.625	0.7501	0.375
15	0.5757	0.8485	0.4243
16	0.4855	0.971	0.5145
17	0.4078	0.8156	0.5922
18	0.3866	0.7731	0.6134
19	0.3243	0.6485	0.6757
20	0.292	0.5839	0.708
21	0.2834	0.5668	0.7166
22	0.2403	0.4805	0.7597
23	0.2404	0.4808	0.7596
24	0.1859	0.3718	0.8141
25	0.2055	0.4109	0.7945
26	0.1923	0.3847	0.8077
27	0.9706	0.05874	0.02937
28	0.9585	0.08299	0.04149
29	0.9417	0.1167	0.05835
30	0.9218	0.1565	0.07824
31	0.9023	0.1954	0.09772
32	0.9118	0.1764	0.08821
33	0.8867	0.2267	0.1133
34	0.9223	0.1553	0.07766
35	0.9029	0.1941	0.09707
36	0.971	0.05797	0.02899
37	0.9594	0.08126	0.04063
38	0.9474	0.1053	0.05264
39	0.9291	0.1419	0.07094
40	0.9081	0.1838	0.09192
41	0.8871	0.2258	0.1129
42	0.9142	0.1715	0.08575
43	0.8908	0.2184	0.1092
44	0.9278	0.1444	0.07218
45	0.9084	0.1833	0.09163
46	0.9189	0.1622	0.0811
47	0.9027	0.1946	0.09731
48	0.8885	0.223	0.1115
49	0.9008	0.1983	0.09915
50	0.8761	0.2478	0.1239
51	0.8578	0.2845	0.1422
52	0.8246	0.3508	0.1754
53	0.9067	0.1865	0.09327
54	0.8986	0.2027	0.1014
55	0.8723	0.2555	0.1277
56	0.8593	0.2814	0.1407
57	0.8352	0.3296	0.1648
58	0.8571	0.2858	0.1429
59	0.8432	0.3135	0.1568
60	0.816	0.368	0.184
61	0.8151	0.3698	0.1849
62	0.8322	0.3356	0.1678
63	0.7927	0.4146	0.2073
64	0.7988	0.4023	0.2012
65	0.8389	0.3223	0.1611
66	0.8067	0.3867	0.1933
67	0.7759	0.4482	0.2241
68	0.738	0.524	0.262
69	0.6881	0.6239	0.3119
70	0.8111	0.3778	0.1889
71	0.7671	0.4657	0.2329
72	0.7686	0.4628	0.2314
73	0.755	0.49	0.245
74	0.8528	0.2944	0.1472
75	0.8152	0.3696	0.1848
76	0.8144	0.3711	0.1856
77	0.837	0.326	0.163
78	0.8756	0.2487	0.1244
79	0.836	0.328	0.164
80	0.7808	0.4385	0.2192
81	0.721	0.5579	0.279
82	0.65	0.7	0.35
83	0.6254	0.7492	0.3746
84	0.6915	0.6169	0.3085
85	0.6069	0.7863	0.3931
86	0.5991	0.8019	0.4009
87	0.5996	0.8009	0.4004
88	0.7694	0.4612	0.2306
89	0.6733	0.6534	0.3267
90	0.6189	0.7623	0.3811
91	0.561	0.8779	0.439
92	0.4302	0.8605	0.5698

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
8 &  0.7051 &  0.5898 &  0.2949 \tabularnewline
9 &  0.7962 &  0.4076 &  0.2038 \tabularnewline
10 &  0.8774 &  0.2452 &  0.1226 \tabularnewline
11 &  0.8057 &  0.3886 &  0.1943 \tabularnewline
12 &  0.7913 &  0.4174 &  0.2087 \tabularnewline
13 &  0.7106 &  0.5788 &  0.2894 \tabularnewline
14 &  0.625 &  0.7501 &  0.375 \tabularnewline
15 &  0.5757 &  0.8485 &  0.4243 \tabularnewline
16 &  0.4855 &  0.971 &  0.5145 \tabularnewline
17 &  0.4078 &  0.8156 &  0.5922 \tabularnewline
18 &  0.3866 &  0.7731 &  0.6134 \tabularnewline
19 &  0.3243 &  0.6485 &  0.6757 \tabularnewline
20 &  0.292 &  0.5839 &  0.708 \tabularnewline
21 &  0.2834 &  0.5668 &  0.7166 \tabularnewline
22 &  0.2403 &  0.4805 &  0.7597 \tabularnewline
23 &  0.2404 &  0.4808 &  0.7596 \tabularnewline
24 &  0.1859 &  0.3718 &  0.8141 \tabularnewline
25 &  0.2055 &  0.4109 &  0.7945 \tabularnewline
26 &  0.1923 &  0.3847 &  0.8077 \tabularnewline
27 &  0.9706 &  0.05874 &  0.02937 \tabularnewline
28 &  0.9585 &  0.08299 &  0.04149 \tabularnewline
29 &  0.9417 &  0.1167 &  0.05835 \tabularnewline
30 &  0.9218 &  0.1565 &  0.07824 \tabularnewline
31 &  0.9023 &  0.1954 &  0.09772 \tabularnewline
32 &  0.9118 &  0.1764 &  0.08821 \tabularnewline
33 &  0.8867 &  0.2267 &  0.1133 \tabularnewline
34 &  0.9223 &  0.1553 &  0.07766 \tabularnewline
35 &  0.9029 &  0.1941 &  0.09707 \tabularnewline
36 &  0.971 &  0.05797 &  0.02899 \tabularnewline
37 &  0.9594 &  0.08126 &  0.04063 \tabularnewline
38 &  0.9474 &  0.1053 &  0.05264 \tabularnewline
39 &  0.9291 &  0.1419 &  0.07094 \tabularnewline
40 &  0.9081 &  0.1838 &  0.09192 \tabularnewline
41 &  0.8871 &  0.2258 &  0.1129 \tabularnewline
42 &  0.9142 &  0.1715 &  0.08575 \tabularnewline
43 &  0.8908 &  0.2184 &  0.1092 \tabularnewline
44 &  0.9278 &  0.1444 &  0.07218 \tabularnewline
45 &  0.9084 &  0.1833 &  0.09163 \tabularnewline
46 &  0.9189 &  0.1622 &  0.0811 \tabularnewline
47 &  0.9027 &  0.1946 &  0.09731 \tabularnewline
48 &  0.8885 &  0.223 &  0.1115 \tabularnewline
49 &  0.9008 &  0.1983 &  0.09915 \tabularnewline
50 &  0.8761 &  0.2478 &  0.1239 \tabularnewline
51 &  0.8578 &  0.2845 &  0.1422 \tabularnewline
52 &  0.8246 &  0.3508 &  0.1754 \tabularnewline
53 &  0.9067 &  0.1865 &  0.09327 \tabularnewline
54 &  0.8986 &  0.2027 &  0.1014 \tabularnewline
55 &  0.8723 &  0.2555 &  0.1277 \tabularnewline
56 &  0.8593 &  0.2814 &  0.1407 \tabularnewline
57 &  0.8352 &  0.3296 &  0.1648 \tabularnewline
58 &  0.8571 &  0.2858 &  0.1429 \tabularnewline
59 &  0.8432 &  0.3135 &  0.1568 \tabularnewline
60 &  0.816 &  0.368 &  0.184 \tabularnewline
61 &  0.8151 &  0.3698 &  0.1849 \tabularnewline
62 &  0.8322 &  0.3356 &  0.1678 \tabularnewline
63 &  0.7927 &  0.4146 &  0.2073 \tabularnewline
64 &  0.7988 &  0.4023 &  0.2012 \tabularnewline
65 &  0.8389 &  0.3223 &  0.1611 \tabularnewline
66 &  0.8067 &  0.3867 &  0.1933 \tabularnewline
67 &  0.7759 &  0.4482 &  0.2241 \tabularnewline
68 &  0.738 &  0.524 &  0.262 \tabularnewline
69 &  0.6881 &  0.6239 &  0.3119 \tabularnewline
70 &  0.8111 &  0.3778 &  0.1889 \tabularnewline
71 &  0.7671 &  0.4657 &  0.2329 \tabularnewline
72 &  0.7686 &  0.4628 &  0.2314 \tabularnewline
73 &  0.755 &  0.49 &  0.245 \tabularnewline
74 &  0.8528 &  0.2944 &  0.1472 \tabularnewline
75 &  0.8152 &  0.3696 &  0.1848 \tabularnewline
76 &  0.8144 &  0.3711 &  0.1856 \tabularnewline
77 &  0.837 &  0.326 &  0.163 \tabularnewline
78 &  0.8756 &  0.2487 &  0.1244 \tabularnewline
79 &  0.836 &  0.328 &  0.164 \tabularnewline
80 &  0.7808 &  0.4385 &  0.2192 \tabularnewline
81 &  0.721 &  0.5579 &  0.279 \tabularnewline
82 &  0.65 &  0.7 &  0.35 \tabularnewline
83 &  0.6254 &  0.7492 &  0.3746 \tabularnewline
84 &  0.6915 &  0.6169 &  0.3085 \tabularnewline
85 &  0.6069 &  0.7863 &  0.3931 \tabularnewline
86 &  0.5991 &  0.8019 &  0.4009 \tabularnewline
87 &  0.5996 &  0.8009 &  0.4004 \tabularnewline
88 &  0.7694 &  0.4612 &  0.2306 \tabularnewline
89 &  0.6733 &  0.6534 &  0.3267 \tabularnewline
90 &  0.6189 &  0.7623 &  0.3811 \tabularnewline
91 &  0.561 &  0.8779 &  0.439 \tabularnewline
92 &  0.4302 &  0.8605 &  0.5698 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=297739&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]8[/C][C] 0.7051[/C][C] 0.5898[/C][C] 0.2949[/C][/ROW]
[ROW][C]9[/C][C] 0.7962[/C][C] 0.4076[/C][C] 0.2038[/C][/ROW]
[ROW][C]10[/C][C] 0.8774[/C][C] 0.2452[/C][C] 0.1226[/C][/ROW]
[ROW][C]11[/C][C] 0.8057[/C][C] 0.3886[/C][C] 0.1943[/C][/ROW]
[ROW][C]12[/C][C] 0.7913[/C][C] 0.4174[/C][C] 0.2087[/C][/ROW]
[ROW][C]13[/C][C] 0.7106[/C][C] 0.5788[/C][C] 0.2894[/C][/ROW]
[ROW][C]14[/C][C] 0.625[/C][C] 0.7501[/C][C] 0.375[/C][/ROW]
[ROW][C]15[/C][C] 0.5757[/C][C] 0.8485[/C][C] 0.4243[/C][/ROW]
[ROW][C]16[/C][C] 0.4855[/C][C] 0.971[/C][C] 0.5145[/C][/ROW]
[ROW][C]17[/C][C] 0.4078[/C][C] 0.8156[/C][C] 0.5922[/C][/ROW]
[ROW][C]18[/C][C] 0.3866[/C][C] 0.7731[/C][C] 0.6134[/C][/ROW]
[ROW][C]19[/C][C] 0.3243[/C][C] 0.6485[/C][C] 0.6757[/C][/ROW]
[ROW][C]20[/C][C] 0.292[/C][C] 0.5839[/C][C] 0.708[/C][/ROW]
[ROW][C]21[/C][C] 0.2834[/C][C] 0.5668[/C][C] 0.7166[/C][/ROW]
[ROW][C]22[/C][C] 0.2403[/C][C] 0.4805[/C][C] 0.7597[/C][/ROW]
[ROW][C]23[/C][C] 0.2404[/C][C] 0.4808[/C][C] 0.7596[/C][/ROW]
[ROW][C]24[/C][C] 0.1859[/C][C] 0.3718[/C][C] 0.8141[/C][/ROW]
[ROW][C]25[/C][C] 0.2055[/C][C] 0.4109[/C][C] 0.7945[/C][/ROW]
[ROW][C]26[/C][C] 0.1923[/C][C] 0.3847[/C][C] 0.8077[/C][/ROW]
[ROW][C]27[/C][C] 0.9706[/C][C] 0.05874[/C][C] 0.02937[/C][/ROW]
[ROW][C]28[/C][C] 0.9585[/C][C] 0.08299[/C][C] 0.04149[/C][/ROW]
[ROW][C]29[/C][C] 0.9417[/C][C] 0.1167[/C][C] 0.05835[/C][/ROW]
[ROW][C]30[/C][C] 0.9218[/C][C] 0.1565[/C][C] 0.07824[/C][/ROW]
[ROW][C]31[/C][C] 0.9023[/C][C] 0.1954[/C][C] 0.09772[/C][/ROW]
[ROW][C]32[/C][C] 0.9118[/C][C] 0.1764[/C][C] 0.08821[/C][/ROW]
[ROW][C]33[/C][C] 0.8867[/C][C] 0.2267[/C][C] 0.1133[/C][/ROW]
[ROW][C]34[/C][C] 0.9223[/C][C] 0.1553[/C][C] 0.07766[/C][/ROW]
[ROW][C]35[/C][C] 0.9029[/C][C] 0.1941[/C][C] 0.09707[/C][/ROW]
[ROW][C]36[/C][C] 0.971[/C][C] 0.05797[/C][C] 0.02899[/C][/ROW]
[ROW][C]37[/C][C] 0.9594[/C][C] 0.08126[/C][C] 0.04063[/C][/ROW]
[ROW][C]38[/C][C] 0.9474[/C][C] 0.1053[/C][C] 0.05264[/C][/ROW]
[ROW][C]39[/C][C] 0.9291[/C][C] 0.1419[/C][C] 0.07094[/C][/ROW]
[ROW][C]40[/C][C] 0.9081[/C][C] 0.1838[/C][C] 0.09192[/C][/ROW]
[ROW][C]41[/C][C] 0.8871[/C][C] 0.2258[/C][C] 0.1129[/C][/ROW]
[ROW][C]42[/C][C] 0.9142[/C][C] 0.1715[/C][C] 0.08575[/C][/ROW]
[ROW][C]43[/C][C] 0.8908[/C][C] 0.2184[/C][C] 0.1092[/C][/ROW]
[ROW][C]44[/C][C] 0.9278[/C][C] 0.1444[/C][C] 0.07218[/C][/ROW]
[ROW][C]45[/C][C] 0.9084[/C][C] 0.1833[/C][C] 0.09163[/C][/ROW]
[ROW][C]46[/C][C] 0.9189[/C][C] 0.1622[/C][C] 0.0811[/C][/ROW]
[ROW][C]47[/C][C] 0.9027[/C][C] 0.1946[/C][C] 0.09731[/C][/ROW]
[ROW][C]48[/C][C] 0.8885[/C][C] 0.223[/C][C] 0.1115[/C][/ROW]
[ROW][C]49[/C][C] 0.9008[/C][C] 0.1983[/C][C] 0.09915[/C][/ROW]
[ROW][C]50[/C][C] 0.8761[/C][C] 0.2478[/C][C] 0.1239[/C][/ROW]
[ROW][C]51[/C][C] 0.8578[/C][C] 0.2845[/C][C] 0.1422[/C][/ROW]
[ROW][C]52[/C][C] 0.8246[/C][C] 0.3508[/C][C] 0.1754[/C][/ROW]
[ROW][C]53[/C][C] 0.9067[/C][C] 0.1865[/C][C] 0.09327[/C][/ROW]
[ROW][C]54[/C][C] 0.8986[/C][C] 0.2027[/C][C] 0.1014[/C][/ROW]
[ROW][C]55[/C][C] 0.8723[/C][C] 0.2555[/C][C] 0.1277[/C][/ROW]
[ROW][C]56[/C][C] 0.8593[/C][C] 0.2814[/C][C] 0.1407[/C][/ROW]
[ROW][C]57[/C][C] 0.8352[/C][C] 0.3296[/C][C] 0.1648[/C][/ROW]
[ROW][C]58[/C][C] 0.8571[/C][C] 0.2858[/C][C] 0.1429[/C][/ROW]
[ROW][C]59[/C][C] 0.8432[/C][C] 0.3135[/C][C] 0.1568[/C][/ROW]
[ROW][C]60[/C][C] 0.816[/C][C] 0.368[/C][C] 0.184[/C][/ROW]
[ROW][C]61[/C][C] 0.8151[/C][C] 0.3698[/C][C] 0.1849[/C][/ROW]
[ROW][C]62[/C][C] 0.8322[/C][C] 0.3356[/C][C] 0.1678[/C][/ROW]
[ROW][C]63[/C][C] 0.7927[/C][C] 0.4146[/C][C] 0.2073[/C][/ROW]
[ROW][C]64[/C][C] 0.7988[/C][C] 0.4023[/C][C] 0.2012[/C][/ROW]
[ROW][C]65[/C][C] 0.8389[/C][C] 0.3223[/C][C] 0.1611[/C][/ROW]
[ROW][C]66[/C][C] 0.8067[/C][C] 0.3867[/C][C] 0.1933[/C][/ROW]
[ROW][C]67[/C][C] 0.7759[/C][C] 0.4482[/C][C] 0.2241[/C][/ROW]
[ROW][C]68[/C][C] 0.738[/C][C] 0.524[/C][C] 0.262[/C][/ROW]
[ROW][C]69[/C][C] 0.6881[/C][C] 0.6239[/C][C] 0.3119[/C][/ROW]
[ROW][C]70[/C][C] 0.8111[/C][C] 0.3778[/C][C] 0.1889[/C][/ROW]
[ROW][C]71[/C][C] 0.7671[/C][C] 0.4657[/C][C] 0.2329[/C][/ROW]
[ROW][C]72[/C][C] 0.7686[/C][C] 0.4628[/C][C] 0.2314[/C][/ROW]
[ROW][C]73[/C][C] 0.755[/C][C] 0.49[/C][C] 0.245[/C][/ROW]
[ROW][C]74[/C][C] 0.8528[/C][C] 0.2944[/C][C] 0.1472[/C][/ROW]
[ROW][C]75[/C][C] 0.8152[/C][C] 0.3696[/C][C] 0.1848[/C][/ROW]
[ROW][C]76[/C][C] 0.8144[/C][C] 0.3711[/C][C] 0.1856[/C][/ROW]
[ROW][C]77[/C][C] 0.837[/C][C] 0.326[/C][C] 0.163[/C][/ROW]
[ROW][C]78[/C][C] 0.8756[/C][C] 0.2487[/C][C] 0.1244[/C][/ROW]
[ROW][C]79[/C][C] 0.836[/C][C] 0.328[/C][C] 0.164[/C][/ROW]
[ROW][C]80[/C][C] 0.7808[/C][C] 0.4385[/C][C] 0.2192[/C][/ROW]
[ROW][C]81[/C][C] 0.721[/C][C] 0.5579[/C][C] 0.279[/C][/ROW]
[ROW][C]82[/C][C] 0.65[/C][C] 0.7[/C][C] 0.35[/C][/ROW]
[ROW][C]83[/C][C] 0.6254[/C][C] 0.7492[/C][C] 0.3746[/C][/ROW]
[ROW][C]84[/C][C] 0.6915[/C][C] 0.6169[/C][C] 0.3085[/C][/ROW]
[ROW][C]85[/C][C] 0.6069[/C][C] 0.7863[/C][C] 0.3931[/C][/ROW]
[ROW][C]86[/C][C] 0.5991[/C][C] 0.8019[/C][C] 0.4009[/C][/ROW]
[ROW][C]87[/C][C] 0.5996[/C][C] 0.8009[/C][C] 0.4004[/C][/ROW]
[ROW][C]88[/C][C] 0.7694[/C][C] 0.4612[/C][C] 0.2306[/C][/ROW]
[ROW][C]89[/C][C] 0.6733[/C][C] 0.6534[/C][C] 0.3267[/C][/ROW]
[ROW][C]90[/C][C] 0.6189[/C][C] 0.7623[/C][C] 0.3811[/C][/ROW]
[ROW][C]91[/C][C] 0.561[/C][C] 0.8779[/C][C] 0.439[/C][/ROW]
[ROW][C]92[/C][C] 0.4302[/C][C] 0.8605[/C][C] 0.5698[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=297739&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=297739&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
8	0.7051	0.5898	0.2949
9	0.7962	0.4076	0.2038
10	0.8774	0.2452	0.1226
11	0.8057	0.3886	0.1943
12	0.7913	0.4174	0.2087
13	0.7106	0.5788	0.2894
14	0.625	0.7501	0.375
15	0.5757	0.8485	0.4243
16	0.4855	0.971	0.5145
17	0.4078	0.8156	0.5922
18	0.3866	0.7731	0.6134
19	0.3243	0.6485	0.6757
20	0.292	0.5839	0.708
21	0.2834	0.5668	0.7166
22	0.2403	0.4805	0.7597
23	0.2404	0.4808	0.7596
24	0.1859	0.3718	0.8141
25	0.2055	0.4109	0.7945
26	0.1923	0.3847	0.8077
27	0.9706	0.05874	0.02937
28	0.9585	0.08299	0.04149
29	0.9417	0.1167	0.05835
30	0.9218	0.1565	0.07824
31	0.9023	0.1954	0.09772
32	0.9118	0.1764	0.08821
33	0.8867	0.2267	0.1133
34	0.9223	0.1553	0.07766
35	0.9029	0.1941	0.09707
36	0.971	0.05797	0.02899
37	0.9594	0.08126	0.04063
38	0.9474	0.1053	0.05264
39	0.9291	0.1419	0.07094
40	0.9081	0.1838	0.09192
41	0.8871	0.2258	0.1129
42	0.9142	0.1715	0.08575
43	0.8908	0.2184	0.1092
44	0.9278	0.1444	0.07218
45	0.9084	0.1833	0.09163
46	0.9189	0.1622	0.0811
47	0.9027	0.1946	0.09731
48	0.8885	0.223	0.1115
49	0.9008	0.1983	0.09915
50	0.8761	0.2478	0.1239
51	0.8578	0.2845	0.1422
52	0.8246	0.3508	0.1754
53	0.9067	0.1865	0.09327
54	0.8986	0.2027	0.1014
55	0.8723	0.2555	0.1277
56	0.8593	0.2814	0.1407
57	0.8352	0.3296	0.1648
58	0.8571	0.2858	0.1429
59	0.8432	0.3135	0.1568
60	0.816	0.368	0.184
61	0.8151	0.3698	0.1849
62	0.8322	0.3356	0.1678
63	0.7927	0.4146	0.2073
64	0.7988	0.4023	0.2012
65	0.8389	0.3223	0.1611
66	0.8067	0.3867	0.1933
67	0.7759	0.4482	0.2241
68	0.738	0.524	0.262
69	0.6881	0.6239	0.3119
70	0.8111	0.3778	0.1889
71	0.7671	0.4657	0.2329
72	0.7686	0.4628	0.2314
73	0.755	0.49	0.245
74	0.8528	0.2944	0.1472
75	0.8152	0.3696	0.1848
76	0.8144	0.3711	0.1856
77	0.837	0.326	0.163
78	0.8756	0.2487	0.1244
79	0.836	0.328	0.164
80	0.7808	0.4385	0.2192
81	0.721	0.5579	0.279
82	0.65	0.7	0.35
83	0.6254	0.7492	0.3746
84	0.6915	0.6169	0.3085
85	0.6069	0.7863	0.3931
86	0.5991	0.8019	0.4009
87	0.5996	0.8009	0.4004
88	0.7694	0.4612	0.2306
89	0.6733	0.6534	0.3267
90	0.6189	0.7623	0.3811
91	0.561	0.8779	0.439
92	0.4302	0.8605	0.5698

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description	# significant tests	% significant tests	OK/NOK
1% type I error level	0	0	OK
5% type I error level	0	0	OK
10% type I error level	4	0.0470588	OK

\begin{tabular}{lllllllll}
\hline
Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
Description & # significant tests & % significant tests & OK/NOK \tabularnewline
1% type I error level & 0 &  0 & OK \tabularnewline
5% type I error level & 0 & 0 & OK \tabularnewline
10% type I error level & 4 & 0.0470588 & OK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=297739&T=6

[TABLE]
[ROW][C]Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]Description[/C][C]# significant tests[/C][C]% significant tests[/C][C]OK/NOK[/C][/ROW]
[ROW][C]1% type I error level[/C][C]0[/C][C] 0[/C][C]OK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]4[/C][C]0.0470588[/C][C]OK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=297739&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=297739&T=6

As an alternative you can also use a QR Code:

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

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description	# significant tests	% significant tests	OK/NOK
1% type I error level	0	0	OK
5% type I error level	0	0	OK
10% type I error level	4	0.0470588	OK

Ramsey RESET F-Test for powers (2 and 3) of fitted values
> reset_test_fitted RESET test data: mylm RESET = 0.47572, df1 = 2, df2 = 93, p-value = 0.6229
Ramsey RESET F-Test for powers (2 and 3) of regressors
> reset_test_regressors RESET test data: mylm RESET = 1.0185, df1 = 8, df2 = 87, p-value = 0.4283
Ramsey RESET F-Test for powers (2 and 3) of principal components
> reset_test_principal_components RESET test data: mylm RESET = 0.60236, df1 = 2, df2 = 93, p-value = 0.5496

\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 = 0.47572, df1 = 2, df2 = 93, p-value = 0.6229
 \tabularnewline
Ramsey RESET F-Test for powers (2 and 3) of regressors \tabularnewline
> reset_test_regressors
	RESET test
data:  mylm
RESET = 1.0185, df1 = 8, df2 = 87, p-value = 0.4283
 \tabularnewline
Ramsey RESET F-Test for powers (2 and 3) of principal components \tabularnewline
> reset_test_principal_components
	RESET test
data:  mylm
RESET = 0.60236, df1 = 2, df2 = 93, p-value = 0.5496
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=297739&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 = 0.47572, df1 = 2, df2 = 93, p-value = 0.6229
[/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 = 1.0185, df1 = 8, df2 = 87, p-value = 0.4283
[/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 = 0.60236, df1 = 2, df2 = 93, p-value = 0.5496
[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=297739&T=7

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=297739&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 = 0.47572, df1 = 2, df2 = 93, p-value = 0.6229
Ramsey RESET F-Test for powers (2 and 3) of regressors
> reset_test_regressors RESET test data: mylm RESET = 1.0185, df1 = 8, df2 = 87, p-value = 0.4283
Ramsey RESET F-Test for powers (2 and 3) of principal components
> reset_test_principal_components RESET test data: mylm RESET = 0.60236, df1 = 2, df2 = 93, p-value = 0.5496

Variance Inflation Factors (Multicollinearity)
> vif Retour Kwaliteit Tevredenheid Verwachtingen 1.358057 1.249211 1.423818 1.113964

\begin{tabular}{lllllllll}
\hline
Variance Inflation Factors (Multicollinearity) \tabularnewline
> vif
       Retour     Kwaliteit  Tevredenheid Verwachtingen 
     1.358057      1.249211      1.423818      1.113964 
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=297739&T=8

[TABLE]
[ROW][C]Variance Inflation Factors (Multicollinearity)[/C][/ROW]
[ROW][C]> vif
       Retour     Kwaliteit  Tevredenheid Verwachtingen 
     1.358057      1.249211      1.423818      1.113964 
[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=297739&T=8

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=297739&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 Retour Kwaliteit Tevredenheid Verwachtingen 1.358057 1.249211 1.423818 1.113964

Figure 1

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

Parameters (R input):

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

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

par5 <- '0'
par4 <- '0'
par3 <- 'No Linear Trend'
par2 <- 'Do not include Seasonal Dummies'
par1 <- '1'
library(lattice)
library(lmtest)
library(car)
library(MASS)
n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
mywarning <- ''
par1 <- as.numeric(par1)
if(is.na(par1)) {
par1 <- 1
mywarning = 'Warning: you did not specify the column number of the endogenous series! The first column was selected by default.'
}
if (par4=='') par4 <- 0
par4 <- as.numeric(par4)
if (par5=='') par5 <- 0
par5 <- as.numeric(par5)
x <- na.omit(t(y))
k <- length(x[1,])
n <- length(x[,1])
x1 <- cbind(x[,par1], x[,1:k!=par1])
mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])
colnames(x1) <- mycolnames #colnames(x)[par1]
x <- x1
if (par3 == 'First Differences'){
(n <- n -1)
x2 <- array(0, dim=c(n,k), dimnames=list(1:n, paste('(1-B)',colnames(x),sep='')))
for (i in 1:n) {
for (j in 1:k) {
x2[i,j] <- x[i+1,j] - x[i,j]
}
}
x <- x2
}
if (par3 == 'Seasonal Differences (s=12)'){
(n <- n - 12)
x2 <- array(0, dim=c(n,k), dimnames=list(1:n, paste('(1-B12)',colnames(x),sep='')))
for (i in 1:n) {
for (j in 1:k) {
x2[i,j] <- x[i+12,j] - x[i,j]
}
}
x <- x2
}
if (par3 == 'First and Seasonal Differences (s=12)'){
(n <- n -1)
x2 <- array(0, dim=c(n,k), dimnames=list(1:n, paste('(1-B)',colnames(x),sep='')))
for (i in 1:n) {
for (j in 1:k) {
x2[i,j] <- x[i+1,j] - x[i,j]
}
}
x <- x2
(n <- n - 12)
x2 <- array(0, dim=c(n,k), dimnames=list(1:n, paste('(1-B12)',colnames(x),sep='')))
for (i in 1:n) {
for (j in 1:k) {
x2[i,j] <- x[i+12,j] - x[i,j]
}
}
x <- x2
}
if(par4 > 0) {
x2 <- array(0, dim=c(n-par4,par4), dimnames=list(1:(n-par4), paste(colnames(x)[par1],'(t-',1:par4,')',sep='')))
for (i in 1:(n-par4)) {
for (j in 1:par4) {
x2[i,j] <- x[i+par4-j,par1]
}
}
x <- cbind(x[(par4+1):n,], x2)
n <- n - par4
}
if(par5 > 0) {
x2 <- array(0, dim=c(n-par5*12,par5), dimnames=list(1:(n-par5*12), paste(colnames(x)[par1],'(t-',1:par5,'s)',sep='')))
for (i in 1:(n-par5*12)) {
for (j in 1:par5) {
x2[i,j] <- x[i+par5*12-j*12,par1]
}
}
x <- cbind(x[(par5*12+1):n,], x2)
n <- n - par5*12
}
if (par2 == 'Include Monthly Dummies'){
x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))
for (i in 1:11){
x2[seq(i,n,12),i] <- 1
}
x <- cbind(x, x2)
}
if (par2 == 'Include Quarterly Dummies'){
x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))
for (i in 1:3){
x2[seq(i,n,4),i] <- 1
}
x <- cbind(x, x2)
}
(k <- length(x[n,]))
if (par3 == 'Linear Trend'){
x <- cbind(x, c(1:n))
colnames(x)[k+1] <- 't'
}
print(x)
(k <- length(x[n,]))
head(x)
df <- as.data.frame(x)
(mylm <- lm(df))
(mysum <- summary(mylm))
if (n > n25) {
kp3 <- k + 3
nmkm3 <- n - k - 3
gqarr <- array(NA, dim=c(nmkm3-kp3+1,3))
numgqtests <- 0
numsignificant1 <- 0
numsignificant5 <- 0
numsignificant10 <- 0
for (mypoint in kp3:nmkm3) {
j <- 0
numgqtests <- numgqtests + 1
for (myalt in c('greater', 'two.sided', 'less')) {
j <- j + 1
gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value
}
if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1
if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1
if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1
}
gqarr
}
bitmap(file='test0.png')
plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index')
points(x[,1]-mysum$resid)
grid()
dev.off()
bitmap(file='test1.png')
plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
grid()
dev.off()
bitmap(file='test2.png')
sresid <- studres(mylm)
hist(sresid, freq=FALSE, main='Distribution of Studentized Residuals')
xfit<-seq(min(sresid),max(sresid),length=40)
yfit<-dnorm(xfit)
lines(xfit, yfit)
grid()
dev.off()
bitmap(file='test3.png')
densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
dev.off()
bitmap(file='test4.png')
qqPlot(mylm, main='QQ Plot')
grid()
dev.off()
(myerror <- as.ts(mysum$resid))
bitmap(file='test5.png')
dum <- cbind(lag(myerror,k=1),myerror)
dum
dum1 <- dum[2:length(myerror),]
dum1
z <- as.data.frame(dum1)
print(z)
plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals')
lines(lowess(z))
abline(lm(z))
grid()
dev.off()
bitmap(file='test6.png')
acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
grid()
dev.off()
bitmap(file='test7.png')
pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
grid()
dev.off()
bitmap(file='test8.png')
opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
plot(mylm, las = 1, sub='Residual Diagnostics')
par(opar)
dev.off()
if (n > n25) {
bitmap(file='test9.png')
plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
grid()
dev.off()
}
load(file='createtable')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
a<-table.row.end(a)
myeq <- colnames(x)[1]
myeq <- paste(myeq, '[t] = ', sep='')
for (i in 1:k){
if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
myeq <- paste(myeq, signif(mysum$coefficients[i,1],6), sep=' ')
if (rownames(mysum$coefficients)[i] != '(Intercept)') {
myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='')
if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
}
}
myeq <- paste(myeq, ' + e[t]')
a<-table.row.start(a)
a<-table.element(a, myeq)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, mywarning)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable1.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Multiple Linear Regression - Ordinary Least Squares', 6, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Variable',header=TRUE)
a<-table.element(a,'Parameter',header=TRUE)
a<-table.element(a,'S.D.',header=TRUE)
a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
a<-table.element(a,'2-tail p-value',header=TRUE)
a<-table.element(a,'1-tail p-value',header=TRUE)
a<-table.row.end(a)
for (i in 1:k){
a<-table.row.start(a)
a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE)
a<-table.element(a,formatC(signif(mysum$coefficients[i,1],5),format='g',flag='+'))
a<-table.element(a,formatC(signif(mysum$coefficients[i,2],5),format='g',flag=' '))
a<-table.element(a,formatC(signif(mysum$coefficients[i,3],4),format='e',flag='+'))
a<-table.element(a,formatC(signif(mysum$coefficients[i,4],4),format='g',flag=' '))
a<-table.element(a,formatC(signif(mysum$coefficients[i,4]/2,4),format='g',flag=' '))
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable2.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple R',1,TRUE)
a<-table.element(a,formatC(signif(sqrt(mysum$r.squared),6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'R-squared',1,TRUE)
a<-table.element(a,formatC(signif(mysum$r.squared,6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Adjusted R-squared',1,TRUE)
a<-table.element(a,formatC(signif(mysum$adj.r.squared,6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (value)',1,TRUE)
a<-table.element(a,formatC(signif(mysum$fstatistic[1],6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
a<-table.element(a, signif(mysum$fstatistic[2],6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE)
a<-table.element(a, signif(mysum$fstatistic[3],6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'p-value',1,TRUE)
a<-table.element(a,formatC(signif(1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]),6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Residual Standard Deviation',1,TRUE)
a<-table.element(a,formatC(signif(mysum$sigma,6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
a<-table.element(a,formatC(signif(sum(myerror*myerror),6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable3.tab')
myr <- as.numeric(mysum$resid)
myr
if(n < 200) {
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Time or Index', 1, TRUE)
a<-table.element(a, 'Actuals', 1, TRUE)
a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
a<-table.row.end(a)
for (i in 1:n) {
a<-table.row.start(a)
a<-table.element(a,i, 1, TRUE)
a<-table.element(a,formatC(signif(x[i],6),format='g',flag=' '))
a<-table.element(a,formatC(signif(x[i]-mysum$resid[i],6),format='g',flag=' '))
a<-table.element(a,formatC(signif(mysum$resid[i],6),format='g',flag=' '))
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable4.tab')
if (n > n25) {
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'p-values',header=TRUE)
a<-table.element(a,'Alternative Hypothesis',3,header=TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'breakpoint index',header=TRUE)
a<-table.element(a,'greater',header=TRUE)
a<-table.element(a,'2-sided',header=TRUE)
a<-table.element(a,'less',header=TRUE)
a<-table.row.end(a)
for (mypoint in kp3:nmkm3) {
a<-table.row.start(a)
a<-table.element(a,mypoint,header=TRUE)
a<-table.element(a,formatC(signif(gqarr[mypoint-kp3+1,1],6),format='g',flag=' '))
a<-table.element(a,formatC(signif(gqarr[mypoint-kp3+1,2],6),format='g',flag=' '))
a<-table.element(a,formatC(signif(gqarr[mypoint-kp3+1,3],6),format='g',flag=' '))
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable5.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Description',header=TRUE)
a<-table.element(a,'# significant tests',header=TRUE)
a<-table.element(a,'% significant tests',header=TRUE)
a<-table.element(a,'OK/NOK',header=TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'1% type I error level',header=TRUE)
a<-table.element(a,signif(numsignificant1,6))
a<-table.element(a,formatC(signif(numsignificant1/numgqtests,6),format='g',flag=' '))
if (numsignificant1/numgqtests < 0.01) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'5% type I error level',header=TRUE)
a<-table.element(a,signif(numsignificant5,6))
a<-table.element(a,signif(numsignificant5/numgqtests,6))
if (numsignificant5/numgqtests < 0.05) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'10% type I error level',header=TRUE)
a<-table.element(a,signif(numsignificant10,6))
a<-table.element(a,signif(numsignificant10/numgqtests,6))
if (numsignificant10/numgqtests < 0.1) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable6.tab')
}
}
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Ramsey RESET F-Test for powers (2 and 3) of fitted values',1,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
reset_test_fitted <- resettest(mylm,power=2:3,type='fitted')
a<-table.element(a,paste('',RC.texteval('reset_test_fitted'),'
',sep=''))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Ramsey RESET F-Test for powers (2 and 3) of regressors',1,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
reset_test_regressors <- resettest(mylm,power=2:3,type='regressor')
a<-table.element(a,paste('',RC.texteval('reset_test_regressors'),'
',sep=''))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Ramsey RESET F-Test for powers (2 and 3) of principal components',1,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
reset_test_principal_components <- resettest(mylm,power=2:3,type='princomp')
a<-table.element(a,paste('',RC.texteval('reset_test_principal_components'),'
',sep=''))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable8.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Variance Inflation Factors (Multicollinearity)',1,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
vif <- vif(mylm)
a<-table.element(a,paste('',RC.texteval('vif'),'
',sep=''))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable9.tab')

Free Statistics

Description of Statistical Computation

Tree of Dependent Computations

Dataset

Tables (Output of Computation)

Figures (Output of Computation)

Input Parameters & R Code