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rwasp_multipleregression.wasp

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Multiple Regression

Date of computation

Mon, 18 Dec 2017 17:49:47 +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/Dec/18/t1513625218cqoxmvvnqvs3id6.htm/, Retrieved Tue, 14 May 2024 22:17:38 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=310230, Retrieved Tue, 14 May 2024 22:17:38 +0000

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-       [Multiple Regression] [Dataset_3_MLRM] [2017-12-18 16:49:47] [453a4fcb74c301cf89bf197d0ef2c60e] [Current]

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57.6	99.5	78
64.3	89.9	100.1
73.1	96	113.2
64.2	86.9	93.1
72.7	85.6	115.4
70.7	82.5	103.3
57.3	80.5	45.1
63.2	82.7	104.7
74.3	87.7	111.3
74	92.2	111.5
74.2	93.9	100.9
67.3	94.5	82.1
71.5	94.8	85.4
71.8	85	97.7
79.3	87.4	106.6
70.4	79.5	92.6
75.4	80.5	109.2
79.2	79.8	110
63.3	78.8	52.5
67	81.5	105.3
73.1	82.6	102.3
77.8	89.5	118.5
72.4	90.7	100
64.2	90.7	74.4
68.9	95.7	89.2
68.9	86.6	91.9
76.1	92.4	107
74.7	86.3	103.6
72.8	84.7	101.8
74.9	83.1	105.1
66.9	82.2	55.5
64.2	84.5	92.1
76	81.2	109.8
80.7	88.2	112.7
72.2	89.1	98.5
67.4	89.1	70.3
72.1	98	84.5
70.8	91.7	91.1
77.2	90.9	107.6
75.4	87.1	102.2
70.5	84.5	96
76.7	83.5	107.3
68.8	85.9	59.9
63	89	90.2
80.6	87.6	116.3
81.7	92.9	115.6
72.2	89.1	92
76.9	96.9	76.5
69.5	104.1	87.9
74	93	95.8
87.5	98	116.9
80.5	85.9	102.9
74.8	84.8	95.8
89.2	81.5	117.3
73.5	85.3	52.8
73.9	79.3	100.1
89.5	82.3	116.3
87.4	87.8	111.8
84.3	95	98.5
86.9	104.4	86.2
79.9	103.5	79.9
78.6	99.5	92.3
89.4	96.6	100.5
85.6	88.1	112.5
81	86.4	101.1
92.8	83.6	121.5
71.4	85.7	49.6
75.5	79.8	104.8
92.2	81.9	120.4
86.7	87.1	108.3
89.5	92	105.2
88.4	106.1	85.7
83.3	108.5	86.8
84.7	101.4	95.1
99	100.1	117
84.1	84.4	100.1
92.4	81.6	112.3
97.6	81.5	119.6
77.4	80.9	51.8
81.2	79.9	105.5
96.5	81.2	119.9
100	90.5	115.4
96.2	91.7	112.8
90.8	102.7	85.1
91.3	104.8	96.2
89.4	98.7	103.6
102.9	100.8	119.9
92.1	93.6	103.7
96.6	88.1	109
105	86.8	119.6
90	80.8	57
89.8	84.6	109.2
100.4	82	112.6
111.3	93.6	126
101.1	99.7	109.7
93.9	102.1	80.1
100.4	106.6	105.8
102.2	95.9	114.1
104.5	92.1	98.3
109.1	85.9	125.3
101.4	79.3	111.6
109.5	83.7	119.7
98.6	84.1	65
88.4	83.2	99
112.3	85	124.5
109.8	93.1	119
92.5	95.4	98.8
94.2	107.3	81.8
80.4	112.5	90.3
83.5	97.8	102
94.2	99.1	119.3
86	85.6	104.3
88.7	87.2	102.8
94.8	86	118.8
81.8	92.7	60.9
79.8	98.8	101
96.6	99.2	122.6
95.7	101.4	122.2
91.8	98.8	95
89.2	113.2	75.6
85.5	119.2	83.1
93.6	107.4	89.8
108.4	111.6	126.1
96.6	94.8	108.6
94.8	97.7	98.9
112.2	87.3	124.3
91.6	91.4	56.8
91.5	93.4	102.7
109.5	90.8	121.7
106.9	96.1	118.2
105.9	102.6	101
103.5	107.7	69
97.3	111.4	88.6
103.2	98.9	109.6
125.7	100.7	128.2
104.4	91	102
113	94.8	122.7
109.2	87.3	110.5
92.4	88.8	54
101.4	92.3	108.1
115.6	90.9	125
107.3	95.2	114.1
105.1	98.2	112.4
102.2	103.5	87.3
99.6	109.7	95.4
102.6	116.4	96.9
122.2	87.5	125.8
99.3	87.2	102
102.8	85.5	112.5
111.7	79	118.9
98.3	81.8	62.7
98.6	78.2	110
109	78.9	114.7
112.8	76.9	124.4
105.5	84.4	111.9
94.8	93.1	77
98.3	101.6	84.1
96.5	97.1	96.5
109.8	99.3	106.8
108	77.8	107.9
106.5	74.3	107.5
111.5	80.4	114.3
104.2	85.3	66.6
93.9	80.1	97.9
109.8	78.8	117.8
117	91.8	123.8
106.5	100	103.3
100.1	108.4	84.2
101.7	101.7	103.6
104	94.4	103.6
112.3	89.5	112.2
111.1	69.8	102.7
107.7	72.5	100.8
114.8	69.1	109.4
101.6	71.9	63.5
93	67	92.3
120.9	63.8	119.2
118.7	73.2	121.5
106.3	74.2	97.6
104.8	84.7	78.3
101.8	97.8	95.6
100.3	87.4	97.9
120	81.8	114.4
111.3	68.6	100.9
103.5	64.9	94.4
118.3	64.1	117.2
101.8	63.6	61
97.3	59.8	95.8
120.3	66.3	116.2
117.5	78.1	118.5
110.9	86.8	94.3
105.3	89	74.4
100.7	111.3	94.9
107.8	99.7	102
119.1	103.7	102.9
112.9	90.4	109.5
108.4	77.6	99.7
123.9	73.9	118.3
101.2	81.5	56.2
103.6	88.2	100.3
119.8	78	116.9
112.9	84.7	108.7
111.8	94.8	93.9
115.6	101.5	85.3
104.8	112.4	85.3
110.5	96.6	102.4
128.8	96.9	121.6
108.6	76.1	91.4
117.1	76.9	110.2
124.6	83.8	112.7
104.2	89.4	55.7
108.3	89.1	100.1

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	9 seconds
R Server	Big Analytics Cloud Computing Center

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input view raw input (R code)  \tabularnewline
Raw Outputview raw output of R engine  \tabularnewline
Computing time9 seconds \tabularnewline
R ServerBig Analytics Cloud Computing Center \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=310230&T=0

[TABLE]
[ROW]

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

Raw Input[/C]

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

Raw Output[/C]

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

Computing time[/C]

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

R Server[/C]

Big Analytics Cloud Computing Center[/C][/ROW] [/TABLE] Source: https://freestatistics.org/blog/index.php?pk=310230&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=310230&T=0

As an alternative you can also use a QR Code:

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

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	9 seconds
R Server	Big Analytics Cloud Computing Center

Multiple Linear Regression - Estimated Regression Equation
Industry[t] = -21.0959 -0.0579831Energy[t] + 0.342735Construction[t] + 0.366597`Industry(t-1)`[t] + 0.267479`Industry(t-2)`[t] + 0.295375`Industry(t-3)`[t] -0.0771239`Industry(t-4)`[t] + 0.170519`Industry(t-5)`[t] + 0.0807419`Industry(t-6)`[t] -0.109078`Industry(t-7)`[t] -0.109899`Industry(t-8)`[t] -0.00766402`Industry(t-9)`[t] -0.06486`Industry(t-10)`[t] + 0.0406214`Industry(t-11)`[t] + 0.0802998`Industry(t-12)`[t] + 1.14217M1[t] -1.41466M2[t] + 2.77657M3[t] -4.3791M4[t] -4.42369M5[t] -1.09154M6[t] + 5.29112M7[t] -11.1986M8[t] -0.085199M9[t] + 2.07059M10[t] -3.99261M11[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Industry[t] =  -21.0959 -0.0579831Energy[t] +  0.342735Construction[t] +  0.366597`Industry(t-1)`[t] +  0.267479`Industry(t-2)`[t] +  0.295375`Industry(t-3)`[t] -0.0771239`Industry(t-4)`[t] +  0.170519`Industry(t-5)`[t] +  0.0807419`Industry(t-6)`[t] -0.109078`Industry(t-7)`[t] -0.109899`Industry(t-8)`[t] -0.00766402`Industry(t-9)`[t] -0.06486`Industry(t-10)`[t] +  0.0406214`Industry(t-11)`[t] +  0.0802998`Industry(t-12)`[t] +  1.14217M1[t] -1.41466M2[t] +  2.77657M3[t] -4.3791M4[t] -4.42369M5[t] -1.09154M6[t] +  5.29112M7[t] -11.1986M8[t] -0.085199M9[t] +  2.07059M10[t] -3.99261M11[t]  + e[t] \tabularnewline
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=310230&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Industry[t] =  -21.0959 -0.0579831Energy[t] +  0.342735Construction[t] +  0.366597`Industry(t-1)`[t] +  0.267479`Industry(t-2)`[t] +  0.295375`Industry(t-3)`[t] -0.0771239`Industry(t-4)`[t] +  0.170519`Industry(t-5)`[t] +  0.0807419`Industry(t-6)`[t] -0.109078`Industry(t-7)`[t] -0.109899`Industry(t-8)`[t] -0.00766402`Industry(t-9)`[t] -0.06486`Industry(t-10)`[t] +  0.0406214`Industry(t-11)`[t] +  0.0802998`Industry(t-12)`[t] +  1.14217M1[t] -1.41466M2[t] +  2.77657M3[t] -4.3791M4[t] -4.42369M5[t] -1.09154M6[t] +  5.29112M7[t] -11.1986M8[t] -0.085199M9[t] +  2.07059M10[t] -3.99261M11[t]  + e[t][/C][/ROW]
[ROW][C][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=310230&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=310230&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
Industry[t] = -21.0959 -0.0579831Energy[t] + 0.342735Construction[t] + 0.366597`Industry(t-1)`[t] + 0.267479`Industry(t-2)`[t] + 0.295375`Industry(t-3)`[t] -0.0771239`Industry(t-4)`[t] + 0.170519`Industry(t-5)`[t] + 0.0807419`Industry(t-6)`[t] -0.109078`Industry(t-7)`[t] -0.109899`Industry(t-8)`[t] -0.00766402`Industry(t-9)`[t] -0.06486`Industry(t-10)`[t] + 0.0406214`Industry(t-11)`[t] + 0.0802998`Industry(t-12)`[t] + 1.14217M1[t] -1.41466M2[t] + 2.77657M3[t] -4.3791M4[t] -4.42369M5[t] -1.09154M6[t] + 5.29112M7[t] -11.1986M8[t] -0.085199M9[t] + 2.07059M10[t] -3.99261M11[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)	-21.1	4.46	-4.7300e+00	4.623e-06	2.311e-06
Energy	-0.05798	0.0322	-1.8010e+00	0.07347	0.03674
Construction	+0.3427	0.04181	+8.1970e+00	5.18e-14	2.59e-14
`Industry(t-1)`	+0.3666	0.0657	+5.5800e+00	9.08e-08	4.54e-08
`Industry(t-2)`	+0.2675	0.06624	+4.0380e+00	8.069e-05	4.035e-05
`Industry(t-3)`	+0.2954	0.07067	+4.1800e+00	4.618e-05	2.309e-05
`Industry(t-4)`	-0.07712	0.07409	-1.0410e+00	0.2993	0.1497
`Industry(t-5)`	+0.1705	0.07477	+2.2810e+00	0.02378	0.01189
`Industry(t-6)`	+0.08074	0.07445	+1.0850e+00	0.2796	0.1398
`Industry(t-7)`	-0.1091	0.07434	-1.4670e+00	0.1441	0.07206
`Industry(t-8)`	-0.1099	0.07421	-1.4810e+00	0.1404	0.07021
`Industry(t-9)`	-0.007664	0.07449	-1.0290e-01	0.9182	0.4591
`Industry(t-10)`	-0.06486	0.06888	-9.4160e-01	0.3477	0.1738
`Industry(t-11)`	+0.04062	0.06659	+6.1000e-01	0.5427	0.2713
`Industry(t-12)`	+0.0803	0.06442	+1.2460e+00	0.2143	0.1071
M1	+1.142	2.321	+4.9210e-01	0.6233	0.3116
M2	-1.415	3.123	-4.5290e-01	0.6512	0.3256
M3	+2.777	3.183	+8.7220e-01	0.3843	0.1921
M4	-4.379	3.103	-1.4110e+00	0.1599	0.07995
M5	-4.424	2.804	-1.5780e+00	0.1165	0.05823
M6	-1.091	2.728	-4.0010e-01	0.6896	0.3448
M7	+5.291	2.352	+2.2490e+00	0.02574	0.01287
M8	-11.2	2.78	-4.0280e+00	8.388e-05	4.194e-05
M9	-0.0852	3.278	-2.5990e-02	0.9793	0.4896
M10	+2.071	3.755	+5.5150e-01	0.582	0.291
M11	-3.993	2.812	-1.4200e+00	0.1575	0.07873

\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) & -21.1 &  4.46 & -4.7300e+00 &  4.623e-06 &  2.311e-06 \tabularnewline
Energy & -0.05798 &  0.0322 & -1.8010e+00 &  0.07347 &  0.03674 \tabularnewline
Construction & +0.3427 &  0.04181 & +8.1970e+00 &  5.18e-14 &  2.59e-14 \tabularnewline
`Industry(t-1)` & +0.3666 &  0.0657 & +5.5800e+00 &  9.08e-08 &  4.54e-08 \tabularnewline
`Industry(t-2)` & +0.2675 &  0.06624 & +4.0380e+00 &  8.069e-05 &  4.035e-05 \tabularnewline
`Industry(t-3)` & +0.2954 &  0.07067 & +4.1800e+00 &  4.618e-05 &  2.309e-05 \tabularnewline
`Industry(t-4)` & -0.07712 &  0.07409 & -1.0410e+00 &  0.2993 &  0.1497 \tabularnewline
`Industry(t-5)` & +0.1705 &  0.07477 & +2.2810e+00 &  0.02378 &  0.01189 \tabularnewline
`Industry(t-6)` & +0.08074 &  0.07445 & +1.0850e+00 &  0.2796 &  0.1398 \tabularnewline
`Industry(t-7)` & -0.1091 &  0.07434 & -1.4670e+00 &  0.1441 &  0.07206 \tabularnewline
`Industry(t-8)` & -0.1099 &  0.07421 & -1.4810e+00 &  0.1404 &  0.07021 \tabularnewline
`Industry(t-9)` & -0.007664 &  0.07449 & -1.0290e-01 &  0.9182 &  0.4591 \tabularnewline
`Industry(t-10)` & -0.06486 &  0.06888 & -9.4160e-01 &  0.3477 &  0.1738 \tabularnewline
`Industry(t-11)` & +0.04062 &  0.06659 & +6.1000e-01 &  0.5427 &  0.2713 \tabularnewline
`Industry(t-12)` & +0.0803 &  0.06442 & +1.2460e+00 &  0.2143 &  0.1071 \tabularnewline
M1 & +1.142 &  2.321 & +4.9210e-01 &  0.6233 &  0.3116 \tabularnewline
M2 & -1.415 &  3.123 & -4.5290e-01 &  0.6512 &  0.3256 \tabularnewline
M3 & +2.777 &  3.183 & +8.7220e-01 &  0.3843 &  0.1921 \tabularnewline
M4 & -4.379 &  3.103 & -1.4110e+00 &  0.1599 &  0.07995 \tabularnewline
M5 & -4.424 &  2.804 & -1.5780e+00 &  0.1165 &  0.05823 \tabularnewline
M6 & -1.091 &  2.728 & -4.0010e-01 &  0.6896 &  0.3448 \tabularnewline
M7 & +5.291 &  2.352 & +2.2490e+00 &  0.02574 &  0.01287 \tabularnewline
M8 & -11.2 &  2.78 & -4.0280e+00 &  8.388e-05 &  4.194e-05 \tabularnewline
M9 & -0.0852 &  3.278 & -2.5990e-02 &  0.9793 &  0.4896 \tabularnewline
M10 & +2.071 &  3.755 & +5.5150e-01 &  0.582 &  0.291 \tabularnewline
M11 & -3.993 &  2.812 & -1.4200e+00 &  0.1575 &  0.07873 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=310230&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]-21.1[/C][C] 4.46[/C][C]-4.7300e+00[/C][C] 4.623e-06[/C][C] 2.311e-06[/C][/ROW]
[ROW][C]Energy[/C][C]-0.05798[/C][C] 0.0322[/C][C]-1.8010e+00[/C][C] 0.07347[/C][C] 0.03674[/C][/ROW]
[ROW][C]Construction[/C][C]+0.3427[/C][C] 0.04181[/C][C]+8.1970e+00[/C][C] 5.18e-14[/C][C] 2.59e-14[/C][/ROW]
[ROW][C]`Industry(t-1)`[/C][C]+0.3666[/C][C] 0.0657[/C][C]+5.5800e+00[/C][C] 9.08e-08[/C][C] 4.54e-08[/C][/ROW]
[ROW][C]`Industry(t-2)`[/C][C]+0.2675[/C][C] 0.06624[/C][C]+4.0380e+00[/C][C] 8.069e-05[/C][C] 4.035e-05[/C][/ROW]
[ROW][C]`Industry(t-3)`[/C][C]+0.2954[/C][C] 0.07067[/C][C]+4.1800e+00[/C][C] 4.618e-05[/C][C] 2.309e-05[/C][/ROW]
[ROW][C]`Industry(t-4)`[/C][C]-0.07712[/C][C] 0.07409[/C][C]-1.0410e+00[/C][C] 0.2993[/C][C] 0.1497[/C][/ROW]
[ROW][C]`Industry(t-5)`[/C][C]+0.1705[/C][C] 0.07477[/C][C]+2.2810e+00[/C][C] 0.02378[/C][C] 0.01189[/C][/ROW]
[ROW][C]`Industry(t-6)`[/C][C]+0.08074[/C][C] 0.07445[/C][C]+1.0850e+00[/C][C] 0.2796[/C][C] 0.1398[/C][/ROW]
[ROW][C]`Industry(t-7)`[/C][C]-0.1091[/C][C] 0.07434[/C][C]-1.4670e+00[/C][C] 0.1441[/C][C] 0.07206[/C][/ROW]
[ROW][C]`Industry(t-8)`[/C][C]-0.1099[/C][C] 0.07421[/C][C]-1.4810e+00[/C][C] 0.1404[/C][C] 0.07021[/C][/ROW]
[ROW][C]`Industry(t-9)`[/C][C]-0.007664[/C][C] 0.07449[/C][C]-1.0290e-01[/C][C] 0.9182[/C][C] 0.4591[/C][/ROW]
[ROW][C]`Industry(t-10)`[/C][C]-0.06486[/C][C] 0.06888[/C][C]-9.4160e-01[/C][C] 0.3477[/C][C] 0.1738[/C][/ROW]
[ROW][C]`Industry(t-11)`[/C][C]+0.04062[/C][C] 0.06659[/C][C]+6.1000e-01[/C][C] 0.5427[/C][C] 0.2713[/C][/ROW]
[ROW][C]`Industry(t-12)`[/C][C]+0.0803[/C][C] 0.06442[/C][C]+1.2460e+00[/C][C] 0.2143[/C][C] 0.1071[/C][/ROW]
[ROW][C]M1[/C][C]+1.142[/C][C] 2.321[/C][C]+4.9210e-01[/C][C] 0.6233[/C][C] 0.3116[/C][/ROW]
[ROW][C]M2[/C][C]-1.415[/C][C] 3.123[/C][C]-4.5290e-01[/C][C] 0.6512[/C][C] 0.3256[/C][/ROW]
[ROW][C]M3[/C][C]+2.777[/C][C] 3.183[/C][C]+8.7220e-01[/C][C] 0.3843[/C][C] 0.1921[/C][/ROW]
[ROW][C]M4[/C][C]-4.379[/C][C] 3.103[/C][C]-1.4110e+00[/C][C] 0.1599[/C][C] 0.07995[/C][/ROW]
[ROW][C]M5[/C][C]-4.424[/C][C] 2.804[/C][C]-1.5780e+00[/C][C] 0.1165[/C][C] 0.05823[/C][/ROW]
[ROW][C]M6[/C][C]-1.091[/C][C] 2.728[/C][C]-4.0010e-01[/C][C] 0.6896[/C][C] 0.3448[/C][/ROW]
[ROW][C]M7[/C][C]+5.291[/C][C] 2.352[/C][C]+2.2490e+00[/C][C] 0.02574[/C][C] 0.01287[/C][/ROW]
[ROW][C]M8[/C][C]-11.2[/C][C] 2.78[/C][C]-4.0280e+00[/C][C] 8.388e-05[/C][C] 4.194e-05[/C][/ROW]
[ROW][C]M9[/C][C]-0.0852[/C][C] 3.278[/C][C]-2.5990e-02[/C][C] 0.9793[/C][C] 0.4896[/C][/ROW]
[ROW][C]M10[/C][C]+2.071[/C][C] 3.755[/C][C]+5.5150e-01[/C][C] 0.582[/C][C] 0.291[/C][/ROW]
[ROW][C]M11[/C][C]-3.993[/C][C] 2.812[/C][C]-1.4200e+00[/C][C] 0.1575[/C][C] 0.07873[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=310230&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=310230&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)	-21.1	4.46	-4.7300e+00	4.623e-06	2.311e-06
Energy	-0.05798	0.0322	-1.8010e+00	0.07347	0.03674
Construction	+0.3427	0.04181	+8.1970e+00	5.18e-14	2.59e-14
`Industry(t-1)`	+0.3666	0.0657	+5.5800e+00	9.08e-08	4.54e-08
`Industry(t-2)`	+0.2675	0.06624	+4.0380e+00	8.069e-05	4.035e-05
`Industry(t-3)`	+0.2954	0.07067	+4.1800e+00	4.618e-05	2.309e-05
`Industry(t-4)`	-0.07712	0.07409	-1.0410e+00	0.2993	0.1497
`Industry(t-5)`	+0.1705	0.07477	+2.2810e+00	0.02378	0.01189
`Industry(t-6)`	+0.08074	0.07445	+1.0850e+00	0.2796	0.1398
`Industry(t-7)`	-0.1091	0.07434	-1.4670e+00	0.1441	0.07206
`Industry(t-8)`	-0.1099	0.07421	-1.4810e+00	0.1404	0.07021
`Industry(t-9)`	-0.007664	0.07449	-1.0290e-01	0.9182	0.4591
`Industry(t-10)`	-0.06486	0.06888	-9.4160e-01	0.3477	0.1738
`Industry(t-11)`	+0.04062	0.06659	+6.1000e-01	0.5427	0.2713
`Industry(t-12)`	+0.0803	0.06442	+1.2460e+00	0.2143	0.1071
M1	+1.142	2.321	+4.9210e-01	0.6233	0.3116
M2	-1.415	3.123	-4.5290e-01	0.6512	0.3256
M3	+2.777	3.183	+8.7220e-01	0.3843	0.1921
M4	-4.379	3.103	-1.4110e+00	0.1599	0.07995
M5	-4.424	2.804	-1.5780e+00	0.1165	0.05823
M6	-1.091	2.728	-4.0010e-01	0.6896	0.3448
M7	+5.291	2.352	+2.2490e+00	0.02574	0.01287
M8	-11.2	2.78	-4.0280e+00	8.388e-05	4.194e-05
M9	-0.0852	3.278	-2.5990e-02	0.9793	0.4896
M10	+2.071	3.755	+5.5150e-01	0.582	0.291
M11	-3.993	2.812	-1.4200e+00	0.1575	0.07873

Multiple Linear Regression - Regression Statistics
Multiple R	0.9804
R-squared	0.9611
Adjusted R-squared	0.9556
F-TEST (value)	172.2
F-TEST (DF numerator)	25
F-TEST (DF denominator)	174
p-value	0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	3.26
Sum Squared Residuals	1849

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R &  0.9804 \tabularnewline
R-squared &  0.9611 \tabularnewline
Adjusted R-squared &  0.9556 \tabularnewline
F-TEST (value) &  172.2 \tabularnewline
F-TEST (DF numerator) & 25 \tabularnewline
F-TEST (DF denominator) & 174 \tabularnewline
p-value &  0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation &  3.26 \tabularnewline
Sum Squared Residuals &  1849 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=310230&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C] 0.9804[/C][/ROW]
[ROW][C]R-squared[/C][C] 0.9611[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C] 0.9556[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C] 172.2[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]25[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]174[/C][/ROW]
[ROW][C]p-value[/C][C] 0[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C] 3.26[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C] 1849[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=310230&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=310230&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	0.9804
R-squared	0.9611
Adjusted R-squared	0.9556
F-TEST (value)	172.2
F-TEST (DF numerator)	25
F-TEST (DF denominator)	174
p-value	0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	3.26
Sum Squared Residuals	1849

Menu of Residual Diagnostics
Description	Link
Histogram	Compute
Central Tendency	Compute
QQ Plot	Compute
Kernel Density Plot	Compute
Skewness/Kurtosis Test	Compute
Skewness-Kurtosis Plot	Compute
Harrell-Davis Plot	Compute
Bootstrap Plot -- Central Tendency	Compute
Blocked Bootstrap Plot -- Central Tendency	Compute
(Partial) Autocorrelation Plot	Compute
Spectral Analysis	Compute
Tukey lambda PPCC Plot	Compute
Box-Cox Normality Plot	Compute
Summary Statistics	Compute

\begin{tabular}{lllllllll}
\hline
Menu of Residual Diagnostics \tabularnewline
Description & Link \tabularnewline
Histogram & Compute \tabularnewline
Central Tendency & Compute \tabularnewline
QQ Plot & Compute \tabularnewline
Kernel Density Plot & Compute \tabularnewline
Skewness/Kurtosis Test & Compute \tabularnewline
Skewness-Kurtosis Plot & Compute \tabularnewline
Harrell-Davis Plot & Compute \tabularnewline
Bootstrap Plot -- Central Tendency & Compute \tabularnewline
Blocked Bootstrap Plot -- Central Tendency & Compute \tabularnewline
(Partial) Autocorrelation Plot & Compute \tabularnewline
Spectral Analysis & Compute \tabularnewline
Tukey lambda PPCC Plot & Compute \tabularnewline
Box-Cox Normality Plot & Compute \tabularnewline
Summary Statistics & Compute \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=310230&T=4

[TABLE]
[ROW][C]Menu of Residual Diagnostics[/C][/ROW]
[ROW][C]Description[/C][C]Link[/C][/ROW]
[ROW][C]Histogram[/C][C]Compute[/C][/ROW]
[ROW][C]Central Tendency[/C][C]Compute[/C][/ROW]
[ROW][C]QQ Plot[/C][C]Compute[/C][/ROW]
[ROW][C]Kernel Density Plot[/C][C]Compute[/C][/ROW]
[ROW][C]Skewness/Kurtosis Test[/C][C]Compute[/C][/ROW]
[ROW][C]Skewness-Kurtosis Plot[/C][C]Compute[/C][/ROW]
[ROW][C]Harrell-Davis Plot[/C][C]Compute[/C][/ROW]
[ROW][C]Bootstrap Plot -- Central Tendency[/C][C]Compute[/C][/ROW]
[ROW][C]Blocked Bootstrap Plot -- Central Tendency[/C][C]Compute[/C][/ROW]
[ROW][C](Partial) Autocorrelation Plot[/C][C]Compute[/C][/ROW]
[ROW][C]Spectral Analysis[/C][C]Compute[/C][/ROW]
[ROW][C]Tukey lambda PPCC Plot[/C][C]Compute[/C][/ROW]
[ROW][C]Box-Cox Normality Plot[/C][C]Compute[/C][/ROW]
[ROW][C]Summary Statistics[/C][C]Compute[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=310230&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=310230&T=4

As an alternative you can also use a QR Code:

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

Menu of Residual Diagnostics
Description	Link
Histogram	Compute
Central Tendency	Compute
QQ Plot	Compute
Kernel Density Plot	Compute
Skewness/Kurtosis Test	Compute
Skewness-Kurtosis Plot	Compute
Harrell-Davis Plot	Compute
Bootstrap Plot -- Central Tendency	Compute
Blocked Bootstrap Plot -- Central Tendency	Compute
(Partial) Autocorrelation Plot	Compute
Spectral Analysis	Compute
Tukey lambda PPCC Plot	Compute
Box-Cox Normality Plot	Compute
Summary Statistics	Compute

Ramsey RESET F-Test for powers (2 and 3) of fitted values
> reset_test_fitted RESET test data: mylm RESET = 6.1643, df1 = 2, df2 = 172, p-value = 0.002597
Ramsey RESET F-Test for powers (2 and 3) of regressors
> reset_test_regressors RESET test data: mylm RESET = 1.1966, df1 = 50, df2 = 124, p-value = 0.2124
Ramsey RESET F-Test for powers (2 and 3) of principal components
> reset_test_principal_components RESET test data: mylm RESET = 4.0833, df1 = 2, df2 = 172, p-value = 0.01851

\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 = 6.1643, df1 = 2, df2 = 172, p-value = 0.002597
 \tabularnewline
Ramsey RESET F-Test for powers (2 and 3) of regressors \tabularnewline
> reset_test_regressors
	RESET test
data:  mylm
RESET = 1.1966, df1 = 50, df2 = 124, p-value = 0.2124
 \tabularnewline
Ramsey RESET F-Test for powers (2 and 3) of principal components \tabularnewline
> reset_test_principal_components
	RESET test
data:  mylm
RESET = 4.0833, df1 = 2, df2 = 172, p-value = 0.01851
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=310230&T=5

[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 = 6.1643, df1 = 2, df2 = 172, p-value = 0.002597
[/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 = 1.1966, df1 = 50, df2 = 124, p-value = 0.2124
[/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 = 4.0833, df1 = 2, df2 = 172, p-value = 0.01851
[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=310230&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=310230&T=5

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 = 6.1643, df1 = 2, df2 = 172, p-value = 0.002597
Ramsey RESET F-Test for powers (2 and 3) of regressors
> reset_test_regressors RESET test data: mylm RESET = 1.1966, df1 = 50, df2 = 124, p-value = 0.2124
Ramsey RESET F-Test for powers (2 and 3) of principal components
> reset_test_principal_components RESET test data: mylm RESET = 4.0833, df1 = 2, df2 = 172, p-value = 0.01851

Variance Inflation Factors (Multicollinearity)

> vif
          Energy     Construction  `Industry(t-1)`  `Industry(t-2)` 
        2.337237        10.649521        19.564716        20.020663 
 `Industry(t-3)`  `Industry(t-4)`  `Industry(t-5)`  `Industry(t-6)` 
       22.556314        24.721827        25.561302        25.387180 
 `Industry(t-7)`  `Industry(t-8)`  `Industry(t-9)` `Industry(t-10)` 
       25.430710        25.484875        25.848342        22.111129 
`Industry(t-11)` `Industry(t-12)`               M1               M2 
       20.826340        19.662371         7.884334        14.279354 
              M3               M4               M5               M6 
       14.832412        14.088812        11.507745        10.895662 
              M7               M8               M9              M10 
        8.097670        11.312616        14.884496        19.523660 
             M11 
       10.953117

\begin{tabular}{lllllllll}
\hline
Variance Inflation Factors (Multicollinearity) \tabularnewline
> vif
          Energy     Construction  `Industry(t-1)`  `Industry(t-2)` 
        2.337237        10.649521        19.564716        20.020663 
 `Industry(t-3)`  `Industry(t-4)`  `Industry(t-5)`  `Industry(t-6)` 
       22.556314        24.721827        25.561302        25.387180 
 `Industry(t-7)`  `Industry(t-8)`  `Industry(t-9)` `Industry(t-10)` 
       25.430710        25.484875        25.848342        22.111129 
`Industry(t-11)` `Industry(t-12)`               M1               M2 
       20.826340        19.662371         7.884334        14.279354 
              M3               M4               M5               M6 
       14.832412        14.088812        11.507745        10.895662 
              M7               M8               M9              M10 
        8.097670        11.312616        14.884496        19.523660 
             M11 
       10.953117 
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=310230&T=6

[TABLE]
[ROW][C]Variance Inflation Factors (Multicollinearity)[/C][/ROW]
[ROW][C]> vif
          Energy     Construction  `Industry(t-1)`  `Industry(t-2)` 
        2.337237        10.649521        19.564716        20.020663 
 `Industry(t-3)`  `Industry(t-4)`  `Industry(t-5)`  `Industry(t-6)` 
       22.556314        24.721827        25.561302        25.387180 
 `Industry(t-7)`  `Industry(t-8)`  `Industry(t-9)` `Industry(t-10)` 
       25.430710        25.484875        25.848342        22.111129 
`Industry(t-11)` `Industry(t-12)`               M1               M2 
       20.826340        19.662371         7.884334        14.279354 
              M3               M4               M5               M6 
       14.832412        14.088812        11.507745        10.895662 
              M7               M8               M9              M10 
        8.097670        11.312616        14.884496        19.523660 
             M11 
       10.953117 
[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=310230&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=310230&T=6

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
          Energy     Construction  `Industry(t-1)`  `Industry(t-2)` 
        2.337237        10.649521        19.564716        20.020663 
 `Industry(t-3)`  `Industry(t-4)`  `Industry(t-5)`  `Industry(t-6)` 
       22.556314        24.721827        25.561302        25.387180 
 `Industry(t-7)`  `Industry(t-8)`  `Industry(t-9)` `Industry(t-10)` 
       25.430710        25.484875        25.848342        22.111129 
`Industry(t-11)` `Industry(t-12)`               M1               M2 
       20.826340        19.662371         7.884334        14.279354 
              M3               M4               M5               M6 
       14.832412        14.088812        11.507745        10.895662 
              M7               M8               M9              M10 
        8.097670        11.312616        14.884496        19.523660 
             M11 
       10.953117

Figure 1

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Parameters (Session):

par1 = 0 ; par2 = no ; par3 = 512 ;

Parameters (R input):

par1 = 1 ; par2 = Include Seasonal Dummies ; par3 = No Linear Trend ; par4 = 12 ; par5 = 0 ; par6 = 12 ;

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

library(lattice)
library(lmtest)
library(car)
library(MASS)
n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
mywarning <- ''
par6 <- as.numeric(par6)
if(is.na(par6)) {
par6 <- 12
mywarning = 'Warning: you did not specify the seasonality. The seasonal period was set to s = 12.'
}
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 (!is.numeric(par4)) par4 <- 0
if (par5=='') par5 <- 0
par5 <- as.numeric(par5)
if (!is.numeric(par5)) par5 <- 0
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)'){
(n <- n - par6)
x2 <- array(0, dim=c(n,k), dimnames=list(1:n, paste('(1-Bs)',colnames(x),sep='')))
for (i in 1:n) {
for (j in 1:k) {
x2[i,j] <- x[i+par6,j] - x[i,j]
}
}
x <- x2
}
if (par3 == 'First and Seasonal Differences (s)'){
(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 - par6)
x2 <- array(0, dim=c(n,k), dimnames=list(1:n, paste('(1-Bs)',colnames(x),sep='')))
for (i in 1:n) {
for (j in 1:k) {
x2[i,j] <- x[i+par6,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*par6,par5), dimnames=list(1:(n-par5*par6), paste(colnames(x)[par1],'(t-',1:par5,'s)',sep='')))
for (i in 1:(n-par5*par6)) {
for (j in 1:par5) {
x2[i,j] <- x[i+par5*par6-j*par6,par1]
}
}
x <- cbind(x[(par5*par6+1):n,], x2)
n <- n - par5*par6
}
if (par2 == 'Include Seasonal Dummies'){
x2 <- array(0, dim=c(n,par6-1), dimnames=list(1:n, paste('M', seq(1:(par6-1)), sep ='')))
for (i in 1:(par6-1)){
x2[seq(i,n,par6),i] <- 1
}
x <- cbind(x, x2)
}
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$resid)
myr
a <-table.start()
a <- table.row.start(a)
a <- table.element(a,'Menu of Residual Diagnostics',2,TRUE)
a <- table.row.end(a)
a <- table.row.start(a)
a <- table.element(a,'Description',1,TRUE)
a <- table.element(a,'Link',1,TRUE)
a <- table.row.end(a)
a <- table.row.start(a)
a <-table.element(a,'Histogram',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_histogram.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a <- table.row.start(a)
a <-table.element(a,'Central Tendency',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_centraltendency.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a <- table.row.start(a)
a <-table.element(a,'QQ Plot',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_fitdistrnorm.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a <- table.row.start(a)
a <-table.element(a,'Kernel Density Plot',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_density.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a <- table.row.start(a)
a <-table.element(a,'Skewness/Kurtosis Test',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_skewness_kurtosis.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a <- table.row.start(a)
a <-table.element(a,'Skewness-Kurtosis Plot',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_skewness_kurtosis_plot.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a <- table.row.start(a)
a <-table.element(a,'Harrell-Davis Plot',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_harrell_davis.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a <- table.row.start(a)
a <-table.element(a,'Bootstrap Plot -- Central Tendency',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_bootstrapplot1.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a <- table.row.start(a)
a <-table.element(a,'Blocked Bootstrap Plot -- Central Tendency',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_bootstrapplot.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a <- table.row.start(a)
a <-table.element(a,'(Partial) Autocorrelation Plot',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_autocorrelation.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a <- table.row.start(a)
a <-table.element(a,'Spectral Analysis',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_spectrum.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a <- table.row.start(a)
a <-table.element(a,'Tukey lambda PPCC Plot',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_tukeylambda.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a <- table.row.start(a)
a <-table.element(a,'Box-Cox Normality Plot',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_boxcoxnorm.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a <- table.row.start(a)
a <- table.element(a,'Summary Statistics',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_summary1.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable7.tab')
if(n < 200) {
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Time or Index', 1, TRUE)
a<-table.element(a, 'Actuals', 1, TRUE)
a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
a<-table.row.end(a)
for (i in 1:n) {
a<-table.row.start(a)
a<-table.element(a,i, 1, TRUE)
a<-table.element(a,formatC(signif(x[i],6),format='g',flag=' '))
a<-table.element(a,formatC(signif(x[i]-mysum$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