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Author's title

Author

*Unverified author*

R Software Module

rwasp_multipleregression.wasp

Title produced by software

Multiple Regression

Date of computation

Sat, 05 Aug 2017 01:07:07 +0200

Cite this page as follows

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2017/Aug/05/t1501888161ptjhd64fimzqc9j.htm/, Retrieved Thu, 09 May 2024 22:05:10 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=306938, Retrieved Thu, 09 May 2024 22:05:10 +0000

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IsPrivate?

No (this computation is public)

User-defined keywords

Estimated Impact

131

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

-       [Multiple Regression] [Leap years accoun...] [2017-08-04 23:07:07] [d41d8cd98f00b204e9800998ecf8427e] [Current]

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80.58064516	1	0	0	0	0	0	0	0	0	0	0	0	0
82	2	1	0	0	0	0	0	0	0	0	0	0	0
79.51612903	3	0	1	0	0	0	0	0	0	0	0	0	0
82.5	4	0	0	1	0	0	0	0	0	0	0	0	0
82	5	0	0	0	1	0	0	0	0	0	0	0	0
81.13333333	6	0	0	0	0	1	0	0	0	0	0	0	0
82.25806452	7	0	0	0	0	0	1	0	0	0	0	0	0
83.51612903	8	0	0	0	0	0	0	1	0	0	0	0	0
75.03333333	9	0	0	0	0	0	0	0	1	0	0	0	0
78.67741935	10	0	0	0	0	0	0	0	0	1	0	0	0
80.8	11	0	0	0	0	0	0	0	0	0	1	0	0
72.12903226	12	0	0	0	0	0	0	0	0	0	0	0	1
83.61290323	13	0	0	0	0	0	0	0	0	0	0	0	0
80.62068966	14	1	0	0	0	0	0	0	0	0	0	0	0
81.29032258	15	0	1	0	0	0	0	0	0	0	0	0	0
82.76666667	16	0	0	1	0	0	0	0	0	0	0	0	0
84.58064516	17	0	0	0	1	0	0	0	0	0	0	0	0
81.5	18	0	0	0	0	1	0	0	0	0	0	0	0
80.06451613	19	0	0	0	0	0	1	0	0	0	0	0	0
82.4516129	20	0	0	0	0	0	0	1	0	0	0	0	0
77.86666667	21	0	0	0	0	0	0	0	1	0	0	0	0
80	22	0	0	0	0	0	0	0	0	1	0	0	0
75.76666667	23	0	0	0	0	0	0	0	0	0	1	0	0
71.70967742	24	0	0	0	0	0	0	0	0	0	0	0	1
80.96774194	25	0	0	0	0	0	0	0	0	0	0	0	0
83.14285714	26	1	0	0	0	0	0	0	0	0	0	0	0
82.12903226	27	0	1	0	0	0	0	0	0	0	0	0	0
86.63333333	28	0	0	1	0	0	0	0	0	0	0	0	0
88.61290323	29	0	0	0	1	0	0	0	0	0	0	0	0
85.33333333	30	0	0	0	0	1	0	0	0	0	0	0	0
88.58064516	31	0	0	0	0	0	1	0	0	0	0	0	0
86.25806452	32	0	0	0	0	0	0	1	0	0	0	0	0
80.23333333	33	0	0	0	0	0	0	0	1	0	0	0	0
83.51612903	34	0	0	0	0	0	0	0	0	1	0	0	0
83.76666667	35	0	0	0	0	0	0	0	0	0	1	0	0
77.51612903	36	0	0	0	0	0	0	0	0	0	0	0	1
89.03225806	37	0	0	0	0	0	0	0	0	0	0	0	0
86.35714286	38	1	0	0	0	0	0	0	0	0	0	0	0
84.22580645	39	0	1	0	0	0	0	0	0	0	0	0	0
91.8	40	0	0	1	0	0	0	0	0	0	0	0	0
89.51612903	41	0	0	0	1	0	0	0	0	0	0	0	0
86.26666667	42	0	0	0	0	1	0	0	0	0	0	0	0
90.74193548	43	0	0	0	0	0	1	0	0	0	0	0	0
90.03225806	44	0	0	0	0	0	0	1	0	0	0	0	0
88.26666667	45	0	0	0	0	0	0	0	1	0	0	0	0
83.51612903	46	0	0	0	0	0	0	0	0	1	0	0	0
82.7	47	0	0	0	0	0	0	0	0	0	1	0	0
78.29032258	48	0	0	0	0	0	0	0	0	0	0	0	1
86.83870968	49	0	0	0	0	0	0	0	0	0	0	0	0
82.21428571	50	1	0	0	0	0	0	0	0	0	0	0	0
89.4516129	51	0	1	0	0	0	0	0	0	0	0	0	0
87.9	52	0	0	1	0	0	0	0	0	0	0	0	0
89.83870968	53	0	0	0	1	0	0	0	0	0	0	0	0
93.43333333	54	0	0	0	0	1	0	0	0	0	0	0	0
89.25806452	55	0	0	0	0	0	1	0	0	0	0	0	0
86.87096774	56	0	0	0	0	0	0	1	0	0	0	0	0
85.3	57	0	0	0	0	0	0	0	1	0	0	0	0
82.70967742	58	0	0	0	0	0	0	0	0	1	0	0	0
83.3	59	0	0	0	0	0	0	0	0	0	1	0	0
77.74193548	60	0	0	0	0	0	0	0	0	0	0	0	1
84.64516129	61	0	0	0	0	0	0	0	0	0	0	0	0
86.51724138	62	1	0	0	0	0	0	0	0	0	0	0	0
91.77419355	63	0	1	0	0	0	0	0	0	0	0	0	0
90.6	64	0	0	1	0	0	0	0	0	0	0	0	0
89.38709677	65	0	0	0	1	0	0	0	0	0	0	0	0
90.73333333	66	0	0	0	0	1	0	0	0	0	0	0	0
93.90322581	67	0	0	0	0	0	1	0	0	0	0	0	0
88.48387097	68	0	0	0	0	0	0	1	0	0	0	0	0
90.5	69	0	0	0	0	0	0	0	1	0	0	0	0
89.41935484	70	0	0	0	0	0	0	0	0	1	0	0	0
88.06666667	71	0	0	0	0	0	0	0	0	0	1	0	0
79.58064516	72	0	0	0	0	0	0	0	0	0	0	0	1
87.19354839	73	0	0	0	0	0	0	0	0	0	0	0	0
87.64285714	74	1	0	0	0	0	0	0	0	0	0	0	0
91.16129032	75	0	1	0	0	0	0	0	0	0	0	0	0
93.46666667	76	0	0	1	0	0	0	0	0	0	0	0	0
93.41935484	77	0	0	0	1	0	0	0	0	0	0	0	0
92.1	78	0	0	0	0	1	0	0	0	0	0	0	0
91.38709677	79	0	0	0	0	0	1	0	0	0	0	0	0
88.77419355	80	0	0	0	0	0	0	1	0	0	0	0	0
92.33333333	81	0	0	0	0	0	0	0	1	0	0	0	0
87.67741935	82	0	0	0	0	0	0	0	0	1	0	0	0
85.73333333	83	0	0	0	0	0	0	0	0	0	1	0	0
82.12903226	84	0	0	0	0	0	0	0	0	0	0	0	1
88.06451613	85	0	0	0	0	0	0	0	0	0	0	0	0
86.57142857	86	1	0	0	0	0	0	0	0	0	0	0	0
89.12903226	87	0	1	0	0	0	0	0	0	0	0	0	0
94.8	88	0	0	1	0	0	0	0	0	0	0	0	0
95.22580645	89	0	0	0	1	0	0	0	0	0	0	0	0
95.83333333	90	0	0	0	0	1	0	0	0	0	0	0	0
96.25806452	91	0	0	0	0	0	1	0	0	0	0	0	0
90.64516129	92	0	0	0	0	0	0	1	0	0	0	0	0
90.8	93	0	0	0	0	0	0	0	1	0	0	0	0
92.4516129	94	0	0	0	0	0	0	0	0	1	0	0	0
89.9	95	0	0	0	0	0	0	0	0	0	1	0	0
84.87096774	96	0	0	0	0	0	0	0	0	0	0	0	1
91.64516129	97	0	0	0	0	0	0	0	0	0	0	0	0
88.32142857	98	1	0	0	0	0	0	0	0	0	0	0	0
95.29032258	99	0	1	0	0	0	0	0	0	0	0	0	0
93.06666667	100	0	0	1	0	0	0	0	0	0	0	0	0
99.64516129	101	0	0	0	1	0	0	0	0	0	0	0	0
97.16666667	102	0	0	0	0	1	0	0	0	0	0	0	0
99.61290323	103	0	0	0	0	0	1	0	0	0	0	0	0
96.70967742	104	0	0	0	0	0	0	1	0	0	0	0	0
98.36666667	105	0	0	0	0	0	0	0	1	0	0	0	0
96.48387097	106	0	0	0	0	0	0	0	0	1	0	0	0
93.13333333	107	0	0	0	0	0	0	0	0	0	1	0	0
87.48387097	108	0	0	0	0	0	0	0	0	0	0	0	1
97.93548387	109	0	0	0	0	0	0	0	0	0	0	0	0
96.03448276	110	1	0	0	0	0	0	0	0	0	0	0	0
98.19354839	111	0	1	0	0	0	0	0	0	0	0	0	0
101.9333333	112	0	0	1	0	0	0	0	0	0	0	0	0
102.1290323	113	0	0	0	1	0	0	0	0	0	0	0	0
103.2	114	0	0	0	0	1	0	0	0	0	0	0	0
100.9032258	115	0	0	0	0	0	1	0	0	0	0	0	0
97.5483871	116	0	0	0	0	0	0	1	0	0	0	0	0
103.8333333	117	0	0	0	0	0	0	0	1	0	0	0	0
97.12903226	118	0	0	0	0	0	0	0	0	1	0	0	0
93.73333333	119	0	0	0	0	0	0	0	0	0	1	0	0
89.03225806	120	0	0	0	0	0	0	0	0	0	0	0	1
96	121	0	0	0	0	0	0	0	0	0	0	0	0
97.14285714	122	1	0	0	0	0	0	0	0	0	0	0	0
98.19354839	123	0	1	0	0	0	0	0	0	0	0	0	0
100.9	124	0	0	1	0	0	0	0	0	0	0	0	0
105.3548387	125	0	0	0	1	0	0	0	0	0	0	0	0
108.4666667	126	0	0	0	0	1	0	0	0	0	0	0	0
104.7096774	127	0	0	0	0	0	1	0	0	0	0	0	0
106.6129032	128	0	0	0	0	0	0	1	0	0	0	0	0
108.8333333	129	0	0	0	0	0	0	0	1	0	0	0	0
100.8387097	130	0	0	0	0	0	0	0	0	1	0	0	0
94.43333333	131	0	0	0	0	0	0	0	0	0	1	0	0
91.83870968	132	0	0	0	0	0	0	0	0	0	0	0	1
98.90322581	133	0	0	0	0	0	0	0	0	0	0	0	0
99.46428571	134	1	0	0	0	0	0	0	0	0	0	0	0
104.8709677	135	0	1	0	0	0	0	0	0	0	0	0	0
107.9333333	136	0	0	1	0	0	0	0	0	0	0	0	0
110.2903226	137	0	0	0	1	0	0	0	0	0	0	0	0
109.5333333	138	0	0	0	0	1	0	0	0	0	0	0	0
111.2903226	139	0	0	0	0	0	1	0	0	0	0	0	0
111.1935484	140	0	0	0	0	0	0	1	0	0	0	0	0
105.1	141	0	0	0	0	0	0	0	1	0	0	0	0
104.2903226	142	0	0	0	0	0	0	0	0	1	0	0	0
99.73333333	143	0	0	0	0	0	0	0	0	0	1	0	0
98.19354839	144	0	0	0	0	0	0	0	0	0	0	0	1
101.7741935	145	0	0	0	0	0	0	0	0	0	0	0	0
99.21428571	146	1	0	0	0	0	0	0	0	0	0	0	0
109.4193548	147	0	1	0	0	0	0	0	0	0	0	0	0
114.8666667	148	0	0	1	0	0	0	0	0	0	0	0	0
111.8064516	149	0	0	0	1	0	0	0	0	0	0	0	0
111.8333333	150	0	0	0	0	1	0	0	0	0	0	0	0
115.2580645	151	0	0	0	0	0	1	0	0	0	0	0	0
112.8387097	152	0	0	0	0	0	0	1	0	0	0	0	0
111.0666667	153	0	0	0	0	0	0	0	1	0	0	0	0
106.6451613	154	0	0	0	0	0	0	0	0	1	0	0	0
104.5333333	155	0	0	0	0	0	0	0	0	0	1	0	0
99.38709677	156	0	0	0	0	0	0	0	0	0	0	0	1
106.7096774	157	0	0	0	0	0	0	0	0	0	0	0	0
106.6896552	158	1	0	0	0	0	0	0	0	0	0	0	0
109.5806452	159	0	1	0	0	0	0	0	0	0	0	0	0
114.1333333	160	0	0	1	0	0	0	0	0	0	0	0	0
119.483871	161	0	0	0	1	0	0	0	0	0	0	0	0
115.6	162	0	0	0	0	1	0	0	0	0	0	0	0
117.2903226	163	0	0	0	0	0	1	0	0	0	0	0	0
114.3225806	164	0	0	0	0	0	0	1	0	0	0	0	0
112.9	165	0	0	0	0	0	0	0	1	0	0	0	0
107.4193548	166	0	0	0	0	0	0	0	0	1	0	0	0
104.5666667	167	0	0	0	0	0	0	0	0	0	1	0	0
102.2903226	168	0	0	0	0	0	0	0	0	0	0	0	1
113.8387097	169	0	0	0	0	0	0	0	0	0	0	0	0
107.7142857	170	1	0	0	0	0	0	0	0	0	0	0	0
115.3548387	171	0	1	0	0	0	0	0	0	0	0	0	0
117.5333333	172	0	0	1	0	0	0	0	0	0	0	0	0
114.1290323	173	0	0	0	1	0	0	0	0	0	0	0	0
115.9666667	174	0	0	0	0	1	0	0	0	0	0	0	0
117.4193548	175	0	0	0	0	0	1	0	0	0	0	0	0
115.483871	176	0	0	0	0	0	0	1	0	0	0	0	0
115	177	0	0	0	0	0	0	0	1	0	0	0	0
111.8387097	178	0	0	0	0	0	0	0	0	1	0	0	0
105.7333333	179	0	0	0	0	0	0	0	0	0	1	0	0
102.4516129	180	0	0	0	0	0	0	0	0	0	0	0	1
107.0967742	181	0	0	0	0	0	0	0	0	0	0	0	0
110.3928571	182	1	0	0	0	0	0	0	0	0	0	0	0
109.9354839	183	0	1	0	0	0	0	0	0	0	0	0	0
120.2	184	0	0	1	0	0	0	0	0	0	0	0	0
115.7741935	185	0	0	0	1	0	0	0	0	0	0	0	0
118.4	186	0	0	0	0	1	0	0	0	0	0	0	0
114	187	0	0	0	0	0	1	0	0	0	0	0	0
129.9032258	188	0	0	0	0	0	0	1	0	0	0	1	0
134.4666667	189	0	0	0	0	0	0	0	1	0	0	1	0
122.2903226	190	0	0	0	0	0	0	0	0	1	0	0	0
116	191	0	0	0	0	0	0	0	0	0	1	0	0
109.483871	192	0	0	0	0	0	0	0	0	0	0	0	1
116.7096774	193	0	0	0	0	0	0	0	0	0	0	0	0
114.8214286	194	1	0	0	0	0	0	0	0	0	0	0	0
126.9354839	195	0	1	0	0	0	0	0	0	0	0	0	0
124.2	196	0	0	1	0	0	0	0	0	0	0	0	0
127.9354839	197	0	0	0	1	0	0	0	0	0	0	0	0
126.1666667	198	0	0	0	0	1	0	0	0	0	0	0	0
127.2258065	199	0	0	0	0	0	1	0	0	0	0	0	0
126.1935484	200	0	0	0	0	0	0	1	0	0	0	0	0
119.2666667	201	0	0	0	0	0	0	0	1	0	0	0	0
118.9677419	202	0	0	0	0	0	0	0	0	1	0	0	0
111.9	203	0	0	0	0	0	0	0	0	0	1	0	0
111.9032258	204	0	0	0	0	0	0	0	0	0	0	0	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=306938&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=306938&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=306938&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
superday[t] = + 74.3573 + 0.210718month[t] -1.19227feb[t] + 2.21993mar[t] + 4.87873apr[t] + 5.4854may[t] + 4.89455jun[t] + 5.12469jul[t] + 3.27832aug[t] + 1.96924sep[t] + 0.00486813oct[t] -3.1512nov[t] + 15.4835RW[t] -7.93649dec[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
superday[t] =  +  74.3573 +  0.210718month[t] -1.19227feb[t] +  2.21993mar[t] +  4.87873apr[t] +  5.4854may[t] +  4.89455jun[t] +  5.12469jul[t] +  3.27832aug[t] +  1.96924sep[t] +  0.00486813oct[t] -3.1512nov[t] +  15.4835RW[t] -7.93649dec[t]  + e[t] \tabularnewline
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=306938&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]superday[t] =  +  74.3573 +  0.210718month[t] -1.19227feb[t] +  2.21993mar[t] +  4.87873apr[t] +  5.4854may[t] +  4.89455jun[t] +  5.12469jul[t] +  3.27832aug[t] +  1.96924sep[t] +  0.00486813oct[t] -3.1512nov[t] +  15.4835RW[t] -7.93649dec[t]  + e[t][/C][/ROW]
[ROW][C][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=306938&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=306938&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
superday[t] = + 74.3573 + 0.210718month[t] -1.19227feb[t] + 2.21993mar[t] + 4.87873apr[t] + 5.4854may[t] + 4.89455jun[t] + 5.12469jul[t] + 3.27832aug[t] + 1.96924sep[t] + 0.00486813oct[t] -3.1512nov[t] + 15.4835RW[t] -7.93649dec[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)	+74.36	0.8671	+8.5750e+01	7.574e-154	3.787e-154
month	+0.2107	0.003878	+5.4340e+01	1.032e-117	5.16e-118
feb	-1.192	1.105	-1.0790e+00	0.2819	0.141
mar	+2.22	1.105	+2.0090e+00	0.04594	0.02297
apr	+4.879	1.105	+4.4150e+00	1.69e-05	8.45e-06
may	+5.485	1.105	+4.9640e+00	1.529e-06	7.647e-07
jun	+4.894	1.105	+4.4290e+00	1.594e-05	7.972e-06
jul	+5.125	1.105	+4.6370e+00	6.558e-06	3.279e-06
aug	+3.278	1.113	+2.9440e+00	0.003643	0.001821
sep	+1.969	1.113	+1.7680e+00	0.07859	0.03929
oct	+0.004868	1.105	+4.4040e-03	0.9965	0.4982
nov	-3.151	1.106	-2.8500e+00	0.00485	0.002425
RW	+15.48	2.373	+6.5240e+00	6.041e-10	3.021e-10
dec	-7.936	1.106	-7.1780e+00	1.551e-11	7.755e-12

\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) & +74.36 &  0.8671 & +8.5750e+01 &  7.574e-154 &  3.787e-154 \tabularnewline
month & +0.2107 &  0.003878 & +5.4340e+01 &  1.032e-117 &  5.16e-118 \tabularnewline
feb & -1.192 &  1.105 & -1.0790e+00 &  0.2819 &  0.141 \tabularnewline
mar & +2.22 &  1.105 & +2.0090e+00 &  0.04594 &  0.02297 \tabularnewline
apr & +4.879 &  1.105 & +4.4150e+00 &  1.69e-05 &  8.45e-06 \tabularnewline
may & +5.485 &  1.105 & +4.9640e+00 &  1.529e-06 &  7.647e-07 \tabularnewline
jun & +4.894 &  1.105 & +4.4290e+00 &  1.594e-05 &  7.972e-06 \tabularnewline
jul & +5.125 &  1.105 & +4.6370e+00 &  6.558e-06 &  3.279e-06 \tabularnewline
aug & +3.278 &  1.113 & +2.9440e+00 &  0.003643 &  0.001821 \tabularnewline
sep & +1.969 &  1.113 & +1.7680e+00 &  0.07859 &  0.03929 \tabularnewline
oct & +0.004868 &  1.105 & +4.4040e-03 &  0.9965 &  0.4982 \tabularnewline
nov & -3.151 &  1.106 & -2.8500e+00 &  0.00485 &  0.002425 \tabularnewline
RW & +15.48 &  2.373 & +6.5240e+00 &  6.041e-10 &  3.021e-10 \tabularnewline
dec & -7.936 &  1.106 & -7.1780e+00 &  1.551e-11 &  7.755e-12 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=306938&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]+74.36[/C][C] 0.8671[/C][C]+8.5750e+01[/C][C] 7.574e-154[/C][C] 3.787e-154[/C][/ROW]
[ROW][C]month[/C][C]+0.2107[/C][C] 0.003878[/C][C]+5.4340e+01[/C][C] 1.032e-117[/C][C] 5.16e-118[/C][/ROW]
[ROW][C]feb[/C][C]-1.192[/C][C] 1.105[/C][C]-1.0790e+00[/C][C] 0.2819[/C][C] 0.141[/C][/ROW]
[ROW][C]mar[/C][C]+2.22[/C][C] 1.105[/C][C]+2.0090e+00[/C][C] 0.04594[/C][C] 0.02297[/C][/ROW]
[ROW][C]apr[/C][C]+4.879[/C][C] 1.105[/C][C]+4.4150e+00[/C][C] 1.69e-05[/C][C] 8.45e-06[/C][/ROW]
[ROW][C]may[/C][C]+5.485[/C][C] 1.105[/C][C]+4.9640e+00[/C][C] 1.529e-06[/C][C] 7.647e-07[/C][/ROW]
[ROW][C]jun[/C][C]+4.894[/C][C] 1.105[/C][C]+4.4290e+00[/C][C] 1.594e-05[/C][C] 7.972e-06[/C][/ROW]
[ROW][C]jul[/C][C]+5.125[/C][C] 1.105[/C][C]+4.6370e+00[/C][C] 6.558e-06[/C][C] 3.279e-06[/C][/ROW]
[ROW][C]aug[/C][C]+3.278[/C][C] 1.113[/C][C]+2.9440e+00[/C][C] 0.003643[/C][C] 0.001821[/C][/ROW]
[ROW][C]sep[/C][C]+1.969[/C][C] 1.113[/C][C]+1.7680e+00[/C][C] 0.07859[/C][C] 0.03929[/C][/ROW]
[ROW][C]oct[/C][C]+0.004868[/C][C] 1.105[/C][C]+4.4040e-03[/C][C] 0.9965[/C][C] 0.4982[/C][/ROW]
[ROW][C]nov[/C][C]-3.151[/C][C] 1.106[/C][C]-2.8500e+00[/C][C] 0.00485[/C][C] 0.002425[/C][/ROW]
[ROW][C]RW[/C][C]+15.48[/C][C] 2.373[/C][C]+6.5240e+00[/C][C] 6.041e-10[/C][C] 3.021e-10[/C][/ROW]
[ROW][C]dec[/C][C]-7.936[/C][C] 1.106[/C][C]-7.1780e+00[/C][C] 1.551e-11[/C][C] 7.755e-12[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=306938&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=306938&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)	+74.36	0.8671	+8.5750e+01	7.574e-154	3.787e-154
month	+0.2107	0.003878	+5.4340e+01	1.032e-117	5.16e-118
feb	-1.192	1.105	-1.0790e+00	0.2819	0.141
mar	+2.22	1.105	+2.0090e+00	0.04594	0.02297
apr	+4.879	1.105	+4.4150e+00	1.69e-05	8.45e-06
may	+5.485	1.105	+4.9640e+00	1.529e-06	7.647e-07
jun	+4.894	1.105	+4.4290e+00	1.594e-05	7.972e-06
jul	+5.125	1.105	+4.6370e+00	6.558e-06	3.279e-06
aug	+3.278	1.113	+2.9440e+00	0.003643	0.001821
sep	+1.969	1.113	+1.7680e+00	0.07859	0.03929
oct	+0.004868	1.105	+4.4040e-03	0.9965	0.4982
nov	-3.151	1.106	-2.8500e+00	0.00485	0.002425
RW	+15.48	2.373	+6.5240e+00	6.041e-10	3.021e-10
dec	-7.936	1.106	-7.1780e+00	1.551e-11	7.755e-12

Multiple Linear Regression - Regression Statistics
Multiple R	0.9734
R-squared	0.9475
Adjusted R-squared	0.9439
F-TEST (value)	264
F-TEST (DF numerator)	13
F-TEST (DF denominator)	190
p-value	0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	3.221
Sum Squared Residuals	1972

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R &  0.9734 \tabularnewline
R-squared &  0.9475 \tabularnewline
Adjusted R-squared &  0.9439 \tabularnewline
F-TEST (value) &  264 \tabularnewline
F-TEST (DF numerator) & 13 \tabularnewline
F-TEST (DF denominator) & 190 \tabularnewline
p-value &  0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation &  3.221 \tabularnewline
Sum Squared Residuals &  1972 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=306938&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C] 0.9734[/C][/ROW]
[ROW][C]R-squared[/C][C] 0.9475[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C] 0.9439[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C] 264[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]13[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]190[/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] 3.221[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C] 1972[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=306938&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=306938&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	0.9734
R-squared	0.9475
Adjusted R-squared	0.9439
F-TEST (value)	264
F-TEST (DF numerator)	13
F-TEST (DF denominator)	190
p-value	0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	3.221
Sum Squared Residuals	1972

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=306938&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=306938&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=306938&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 = 41.167, df1 = 2, df2 = 188, p-value = 1.487e-15
Ramsey RESET F-Test for powers (2 and 3) of regressors
> reset_test_regressors RESET test data: mylm RESET = 2.3744, df1 = 26, df2 = 164, p-value = 0.0005515
Ramsey RESET F-Test for powers (2 and 3) of principal components
> reset_test_principal_components RESET test data: mylm RESET = 35.384, df1 = 2, df2 = 188, p-value = 9.06e-14

\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 = 41.167, df1 = 2, df2 = 188, p-value = 1.487e-15
 \tabularnewline
Ramsey RESET F-Test for powers (2 and 3) of regressors \tabularnewline
> reset_test_regressors
	RESET test
data:  mylm
RESET = 2.3744, df1 = 26, df2 = 164, p-value = 0.0005515
 \tabularnewline
Ramsey RESET F-Test for powers (2 and 3) of principal components \tabularnewline
> reset_test_principal_components
	RESET test
data:  mylm
RESET = 35.384, df1 = 2, df2 = 188, p-value = 9.06e-14
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=306938&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 = 41.167, df1 = 2, df2 = 188, p-value = 1.487e-15
[/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 = 2.3744, df1 = 26, df2 = 164, p-value = 0.0005515
[/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 = 35.384, df1 = 2, df2 = 188, p-value = 9.06e-14
[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=306938&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=306938&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 = 41.167, df1 = 2, df2 = 188, p-value = 1.487e-15
Ramsey RESET F-Test for powers (2 and 3) of regressors
> reset_test_regressors RESET test data: mylm RESET = 2.3744, df1 = 26, df2 = 164, p-value = 0.0005515
Ramsey RESET F-Test for powers (2 and 3) of principal components
> reset_test_principal_components RESET test data: mylm RESET = 35.384, df1 = 2, df2 = 188, p-value = 9.06e-14

Variance Inflation Factors (Multicollinearity)
> vif month feb mar apr may jun jul aug 1.025253 1.833356 1.833424 1.833537 1.833695 1.833898 1.834146 1.862048 sep oct nov RW dec 1.862150 1.835163 1.835592 1.074945 1.836066

\begin{tabular}{lllllllll}
\hline
Variance Inflation Factors (Multicollinearity) \tabularnewline
> vif
   month      feb      mar      apr      may      jun      jul      aug 
1.025253 1.833356 1.833424 1.833537 1.833695 1.833898 1.834146 1.862048 
     sep      oct      nov       RW      dec 
1.862150 1.835163 1.835592 1.074945 1.836066 
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=306938&T=6

[TABLE]
[ROW][C]Variance Inflation Factors (Multicollinearity)[/C][/ROW]
[ROW][C]> vif
   month      feb      mar      apr      may      jun      jul      aug 
1.025253 1.833356 1.833424 1.833537 1.833695 1.833898 1.834146 1.862048 
     sep      oct      nov       RW      dec 
1.862150 1.835163 1.835592 1.074945 1.836066 
[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=306938&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=306938&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 month feb mar apr may jun jul aug 1.025253 1.833356 1.833424 1.833537 1.833695 1.833898 1.834146 1.862048 sep oct nov RW dec 1.862150 1.835163 1.835592 1.074945 1.836066

Figure 1

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Figure 10

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Parameters (Session):

par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ; par4 = 1 ; par6 = 12 ;

Parameters (R input):

par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ; par4 = ; par5 = ; par6 = 12 ;

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

par6 <- '12'
par5 <- ''
par4 <- ''
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 <- ''
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