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Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationFri, 20 Nov 2009 08:22:30 -0700
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2009/Nov/20/t1258730623wo2mbbhzm8o6iip.htm/, Retrieved Fri, 29 Mar 2024 09:04:36 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=58265, Retrieved Fri, 29 Mar 2024 09:04:36 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywordsmodel 4
Estimated Impact114
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [Q1 The Seatbeltlaw] [2007-11-14 19:27:43] [8cd6641b921d30ebe00b648d1481bba0]
- RM D  [Multiple Regression] [Seatbelt] [2009-11-12 14:10:54] [b98453cac15ba1066b407e146608df68]
-    D      [Multiple Regression] [workshop 3] [2009-11-20 15:22:30] [0852d9c28828e87a0aee4d255e088d63] [Current]
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Dataseries X:
109.5	120.1	109.5	110.2	108.8	108.2
116	132.9	109.5	109.5	110.2	108.8
111.2	128.1	116	109.5	109.5	110.2
112.1	129.3	111.2	116	109.5	109.5
114	132.5	112.1	111.2	116	109.5
119.1	131	114	112.1	111.2	116
114.1	124.9	119.1	114	112.1	111.2
115.1	120.8	114.1	119.1	114	112.1
115.4	122	115.1	114.1	119.1	114
110.8	122.1	115.4	115.1	114.1	119.1
116	127.4	110.8	115.4	115.1	114.1
119.2	135.2	116	110.8	115.4	115.1
126.5	137.3	119.2	116	110.8	115.4
127.8	135	126.5	119.2	116	110.8
131.3	136	127.8	126.5	119.2	116
140.3	138.4	131.3	127.8	126.5	119.2
137.3	134.7	140.3	131.3	127.8	126.5
143	138.4	137.3	140.3	131.3	127.8
134.5	133.9	143	137.3	140.3	131.3
139.9	133.6	134.5	143	137.3	140.3
159.3	141.2	139.9	134.5	143	137.3
170.4	151.8	159.3	139.9	134.5	143
175	155.4	170.4	159.3	139.9	134.5
175.8	156.6	175	170.4	159.3	139.9
180.9	161.6	175.8	175	170.4	159.3
180.3	160.7	180.9	175.8	175	170.4
169.6	156	180.3	180.9	175.8	175
172.3	159.5	169.6	180.3	180.9	175.8
184.8	168.7	172.3	169.6	180.3	180.9
177.7	169.9	184.8	172.3	169.6	180.3
184.6	169.9	177.7	184.8	172.3	169.6
211.4	185.9	184.6	177.7	184.8	172.3
215.3	190.8	211.4	184.6	177.7	184.8
215.9	195.8	215.3	211.4	184.6	177.7
244.7	211.9	215.9	215.3	211.4	184.6
259.3	227.1	244.7	215.9	215.3	211.4
289	251.3	259.3	244.7	215.9	215.3
310.9	256.7	289	259.3	244.7	215.9
321	251.9	310.9	289	259.3	244.7
315.1	251.2	321	310.9	289	259.3
333.2	270.3	315.1	321	310.9	289
314.1	267.2	333.2	315.1	321	310.9
284.7	243	314.1	333.2	315.1	321
273.9	229.9	284.7	314.1	333.2	315.1
216	187.2	273.9	284.7	314.1	333.2
196.4	178.2	216	273.9	284.7	314.1
190.9	175.2	196.4	216	273.9	284.7
206.4	192.4	190.9	196.4	216	273.9
196.3	187	206.4	190.9	196.4	216
199.5	184	196.3	206.4	190.9	196.4
198.9	194.1	199.5	196.3	206.4	190.9
214.4	212.7	198.9	199.5	196.3	206.4
214.2	217.5	214.4	198.9	199.5	196.3
187.6	200.5	214.2	214.4	198.9	199.5
180.6	205.9	187.6	214.2	214.4	198.9
172.2	196.5	180.6	187.6	214.2	214.4




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time3 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 3 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58265&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]3 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gwilym Jenkins' @ 72.249.127.135[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58265&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58265&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 Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time3 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135







Multiple Linear Regression - Estimated Regression Equation
Y[t] = -28.8691767006593 + 0.722631087194356X[t] + 0.68166492131029Y1[t] + 0.0357487587076676Y2[t] -0.136738073603211Y3[t] -0.0259058241864742Y4[t] -4.2607956589844M1[t] -2.90302129402693M2[t] -6.12901499458149M3[t] -3.79239104204496M4[t] -4.25182903906736M5[t] -13.9408302154570M6[t] -11.8293883425912M7[t] -0.0234283729133178M8[t] -2.79353232120853M9[t] -2.46252649385462M10[t] + 5.15904113856754M11[t] -0.3517093324785t + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Y[t] =  -28.8691767006593 +  0.722631087194356X[t] +  0.68166492131029Y1[t] +  0.0357487587076676Y2[t] -0.136738073603211Y3[t] -0.0259058241864742Y4[t] -4.2607956589844M1[t] -2.90302129402693M2[t] -6.12901499458149M3[t] -3.79239104204496M4[t] -4.25182903906736M5[t] -13.9408302154570M6[t] -11.8293883425912M7[t] -0.0234283729133178M8[t] -2.79353232120853M9[t] -2.46252649385462M10[t] +  5.15904113856754M11[t] -0.3517093324785t  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58265&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Y[t] =  -28.8691767006593 +  0.722631087194356X[t] +  0.68166492131029Y1[t] +  0.0357487587076676Y2[t] -0.136738073603211Y3[t] -0.0259058241864742Y4[t] -4.2607956589844M1[t] -2.90302129402693M2[t] -6.12901499458149M3[t] -3.79239104204496M4[t] -4.25182903906736M5[t] -13.9408302154570M6[t] -11.8293883425912M7[t] -0.0234283729133178M8[t] -2.79353232120853M9[t] -2.46252649385462M10[t] +  5.15904113856754M11[t] -0.3517093324785t  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58265&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58265&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
Y[t] = -28.8691767006593 + 0.722631087194356X[t] + 0.68166492131029Y1[t] + 0.0357487587076676Y2[t] -0.136738073603211Y3[t] -0.0259058241864742Y4[t] -4.2607956589844M1[t] -2.90302129402693M2[t] -6.12901499458149M3[t] -3.79239104204496M4[t] -4.25182903906736M5[t] -13.9408302154570M6[t] -11.8293883425912M7[t] -0.0234283729133178M8[t] -2.79353232120853M9[t] -2.46252649385462M10[t] + 5.15904113856754M11[t] -0.3517093324785t + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)-28.869176700659311.622676-2.48390.0175220.008761
X0.7226310871943560.1417295.09871e-055e-06
Y10.681664921310290.1619334.20950.0001517.5e-05
Y20.03574875870766760.1987510.17990.8582120.429106
Y3-0.1367380736032110.201583-0.67830.5016790.250839
Y4-0.02590582418647420.132452-0.19560.8459760.422988
M1-4.26079565898446.930106-0.61480.5423350.271168
M2-2.903021294026937.122116-0.40760.685850.342925
M3-6.129014994581497.187253-0.85280.3991350.199567
M4-3.792391042044967.243078-0.52360.6036050.301803
M5-4.251829039067367.064731-0.60180.5508560.275428
M6-13.94083021545706.960589-2.00280.0523680.026184
M7-11.82938834259127.515352-1.5740.1237710.061885
M8-0.02342837291331787.466075-0.00310.9975130.498756
M9-2.793532321208537.628985-0.36620.7162660.358133
M10-2.462526493854627.717026-0.31910.7513960.375698
M115.159041138567547.6512880.67430.5042210.25211
t-0.35170933247850.176384-1.9940.0533650.026682

\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) & -28.8691767006593 & 11.622676 & -2.4839 & 0.017522 & 0.008761 \tabularnewline
X & 0.722631087194356 & 0.141729 & 5.0987 & 1e-05 & 5e-06 \tabularnewline
Y1 & 0.68166492131029 & 0.161933 & 4.2095 & 0.000151 & 7.5e-05 \tabularnewline
Y2 & 0.0357487587076676 & 0.198751 & 0.1799 & 0.858212 & 0.429106 \tabularnewline
Y3 & -0.136738073603211 & 0.201583 & -0.6783 & 0.501679 & 0.250839 \tabularnewline
Y4 & -0.0259058241864742 & 0.132452 & -0.1956 & 0.845976 & 0.422988 \tabularnewline
M1 & -4.2607956589844 & 6.930106 & -0.6148 & 0.542335 & 0.271168 \tabularnewline
M2 & -2.90302129402693 & 7.122116 & -0.4076 & 0.68585 & 0.342925 \tabularnewline
M3 & -6.12901499458149 & 7.187253 & -0.8528 & 0.399135 & 0.199567 \tabularnewline
M4 & -3.79239104204496 & 7.243078 & -0.5236 & 0.603605 & 0.301803 \tabularnewline
M5 & -4.25182903906736 & 7.064731 & -0.6018 & 0.550856 & 0.275428 \tabularnewline
M6 & -13.9408302154570 & 6.960589 & -2.0028 & 0.052368 & 0.026184 \tabularnewline
M7 & -11.8293883425912 & 7.515352 & -1.574 & 0.123771 & 0.061885 \tabularnewline
M8 & -0.0234283729133178 & 7.466075 & -0.0031 & 0.997513 & 0.498756 \tabularnewline
M9 & -2.79353232120853 & 7.628985 & -0.3662 & 0.716266 & 0.358133 \tabularnewline
M10 & -2.46252649385462 & 7.717026 & -0.3191 & 0.751396 & 0.375698 \tabularnewline
M11 & 5.15904113856754 & 7.651288 & 0.6743 & 0.504221 & 0.25211 \tabularnewline
t & -0.3517093324785 & 0.176384 & -1.994 & 0.053365 & 0.026682 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58265&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]-28.8691767006593[/C][C]11.622676[/C][C]-2.4839[/C][C]0.017522[/C][C]0.008761[/C][/ROW]
[ROW][C]X[/C][C]0.722631087194356[/C][C]0.141729[/C][C]5.0987[/C][C]1e-05[/C][C]5e-06[/C][/ROW]
[ROW][C]Y1[/C][C]0.68166492131029[/C][C]0.161933[/C][C]4.2095[/C][C]0.000151[/C][C]7.5e-05[/C][/ROW]
[ROW][C]Y2[/C][C]0.0357487587076676[/C][C]0.198751[/C][C]0.1799[/C][C]0.858212[/C][C]0.429106[/C][/ROW]
[ROW][C]Y3[/C][C]-0.136738073603211[/C][C]0.201583[/C][C]-0.6783[/C][C]0.501679[/C][C]0.250839[/C][/ROW]
[ROW][C]Y4[/C][C]-0.0259058241864742[/C][C]0.132452[/C][C]-0.1956[/C][C]0.845976[/C][C]0.422988[/C][/ROW]
[ROW][C]M1[/C][C]-4.2607956589844[/C][C]6.930106[/C][C]-0.6148[/C][C]0.542335[/C][C]0.271168[/C][/ROW]
[ROW][C]M2[/C][C]-2.90302129402693[/C][C]7.122116[/C][C]-0.4076[/C][C]0.68585[/C][C]0.342925[/C][/ROW]
[ROW][C]M3[/C][C]-6.12901499458149[/C][C]7.187253[/C][C]-0.8528[/C][C]0.399135[/C][C]0.199567[/C][/ROW]
[ROW][C]M4[/C][C]-3.79239104204496[/C][C]7.243078[/C][C]-0.5236[/C][C]0.603605[/C][C]0.301803[/C][/ROW]
[ROW][C]M5[/C][C]-4.25182903906736[/C][C]7.064731[/C][C]-0.6018[/C][C]0.550856[/C][C]0.275428[/C][/ROW]
[ROW][C]M6[/C][C]-13.9408302154570[/C][C]6.960589[/C][C]-2.0028[/C][C]0.052368[/C][C]0.026184[/C][/ROW]
[ROW][C]M7[/C][C]-11.8293883425912[/C][C]7.515352[/C][C]-1.574[/C][C]0.123771[/C][C]0.061885[/C][/ROW]
[ROW][C]M8[/C][C]-0.0234283729133178[/C][C]7.466075[/C][C]-0.0031[/C][C]0.997513[/C][C]0.498756[/C][/ROW]
[ROW][C]M9[/C][C]-2.79353232120853[/C][C]7.628985[/C][C]-0.3662[/C][C]0.716266[/C][C]0.358133[/C][/ROW]
[ROW][C]M10[/C][C]-2.46252649385462[/C][C]7.717026[/C][C]-0.3191[/C][C]0.751396[/C][C]0.375698[/C][/ROW]
[ROW][C]M11[/C][C]5.15904113856754[/C][C]7.651288[/C][C]0.6743[/C][C]0.504221[/C][C]0.25211[/C][/ROW]
[ROW][C]t[/C][C]-0.3517093324785[/C][C]0.176384[/C][C]-1.994[/C][C]0.053365[/C][C]0.026682[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58265&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58265&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
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)-28.869176700659311.622676-2.48390.0175220.008761
X0.7226310871943560.1417295.09871e-055e-06
Y10.681664921310290.1619334.20950.0001517.5e-05
Y20.03574875870766760.1987510.17990.8582120.429106
Y3-0.1367380736032110.201583-0.67830.5016790.250839
Y4-0.02590582418647420.132452-0.19560.8459760.422988
M1-4.26079565898446.930106-0.61480.5423350.271168
M2-2.903021294026937.122116-0.40760.685850.342925
M3-6.129014994581497.187253-0.85280.3991350.199567
M4-3.792391042044967.243078-0.52360.6036050.301803
M5-4.251829039067367.064731-0.60180.5508560.275428
M6-13.94083021545706.960589-2.00280.0523680.026184
M7-11.82938834259127.515352-1.5740.1237710.061885
M8-0.02342837291331787.466075-0.00310.9975130.498756
M9-2.793532321208537.628985-0.36620.7162660.358133
M10-2.462526493854627.717026-0.31910.7513960.375698
M115.159041138567547.6512880.67430.5042210.25211
t-0.35170933247850.176384-1.9940.0533650.026682







Multiple Linear Regression - Regression Statistics
Multiple R0.990946497626736
R-squared0.981974961158695
Adjusted R-squared0.973911127992848
F-TEST (value)121.775208013687
F-TEST (DF numerator)17
F-TEST (DF denominator)38
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation10.1208126903771
Sum Squared Residuals3892.3722815205

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.990946497626736 \tabularnewline
R-squared & 0.981974961158695 \tabularnewline
Adjusted R-squared & 0.973911127992848 \tabularnewline
F-TEST (value) & 121.775208013687 \tabularnewline
F-TEST (DF numerator) & 17 \tabularnewline
F-TEST (DF denominator) & 38 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 10.1208126903771 \tabularnewline
Sum Squared Residuals & 3892.3722815205 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58265&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.990946497626736[/C][/ROW]
[ROW][C]R-squared[/C][C]0.981974961158695[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.973911127992848[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]121.775208013687[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]17[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]38[/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]10.1208126903771[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]3892.3722815205[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58265&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58265&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 R0.990946497626736
R-squared0.981974961158695
Adjusted R-squared0.973911127992848
F-TEST (value)121.775208013687
F-TEST (DF numerator)17
F-TEST (DF denominator)38
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation10.1208126903771
Sum Squared Residuals3892.3722815205







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
1109.5114.208021387977-4.70802138797678
2116124.231763407891-8.23176340789096
3111.2121.675701642503-10.4757016425032
4112.1121.506282953435-9.40628295343544
5114122.560662011918-8.56066201191806
6119.1113.2512970016685.84870299833185
7114.1114.148677340249-0.0486773402491406
8115.1119.131017001195-4.03101700119537
9115.4116.632696911496-1.23269691149615
10110.8117.476075414857-6.67607541485681
11116125.443735513845-9.44373551384466
12119.2128.882793577405-9.68279357740541
13126.5128.776258553842-2.27625855384229
14127.8132.618950847725-4.81895084772499
15131.3130.3387371168560.961262883144313
16140.3135.4091803823864.89081961761399
17137.3137.81753096529-0.517530965289984
18143128.21503871442514.7849612855745
19134.5129.1798620907025.32013790929774
20139.9135.0040012983714.89599870162908
21159.3140.04962085935619.2503791406442
22170.4162.1207600766978.27923992330312
23175179.733910744141-4.73391074414088
24175.8175.830177358901-0.0301773589012391
25180.9173.5202384243007.37976157569961
26180.3176.4644757969083.83552420309158
27169.6169.0351691205440.564830879455514
28172.3165.5159397978126.78406020218753
29184.8172.76090518067612.0390948193237
30177.7173.6833260233974.01667397660301
31184.6170.95809662639413.6419033736063
32211.4196.64494478357614.7550552164243
33215.3216.226327676505-0.926327676504908
34215.9222.675778177690-6.77577817768954
35244.7238.2850855337566.4149144662443
36259.3262.184172001775-2.88417200177477
37289285.8581358638373.14186413616275
38310.9307.5801887929293.31981120707104
39321313.7815928404997.21840715950129
40315.1318.489033401315-3.38903340131464
41333.2324.0554124741949.14458752580584
42314.1321.953370901288-7.85337090128819
43284.7294.39778947713-9.69778947712996
44273.9273.3397081247260.560291875273653
45216233.091354552643-17.0913545526431
46196.4191.2273863307575.17261366924323
47190.9183.1372682082597.76273179174125
48206.4193.80285706191912.5971429380814
49196.3199.837345770043-3.53734577004328
50199.5193.6046211545475.89537884545333
51198.9197.1687992795981.73120072040208
52214.4213.2795634650511.12043653494857
53214.2226.305489367921-12.1054893679215
54187.6204.396967359221-16.7969673592212
55180.6189.815574465525-9.21557446552495
56172.2188.380328792132-16.1803287921317

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 109.5 & 114.208021387977 & -4.70802138797678 \tabularnewline
2 & 116 & 124.231763407891 & -8.23176340789096 \tabularnewline
3 & 111.2 & 121.675701642503 & -10.4757016425032 \tabularnewline
4 & 112.1 & 121.506282953435 & -9.40628295343544 \tabularnewline
5 & 114 & 122.560662011918 & -8.56066201191806 \tabularnewline
6 & 119.1 & 113.251297001668 & 5.84870299833185 \tabularnewline
7 & 114.1 & 114.148677340249 & -0.0486773402491406 \tabularnewline
8 & 115.1 & 119.131017001195 & -4.03101700119537 \tabularnewline
9 & 115.4 & 116.632696911496 & -1.23269691149615 \tabularnewline
10 & 110.8 & 117.476075414857 & -6.67607541485681 \tabularnewline
11 & 116 & 125.443735513845 & -9.44373551384466 \tabularnewline
12 & 119.2 & 128.882793577405 & -9.68279357740541 \tabularnewline
13 & 126.5 & 128.776258553842 & -2.27625855384229 \tabularnewline
14 & 127.8 & 132.618950847725 & -4.81895084772499 \tabularnewline
15 & 131.3 & 130.338737116856 & 0.961262883144313 \tabularnewline
16 & 140.3 & 135.409180382386 & 4.89081961761399 \tabularnewline
17 & 137.3 & 137.81753096529 & -0.517530965289984 \tabularnewline
18 & 143 & 128.215038714425 & 14.7849612855745 \tabularnewline
19 & 134.5 & 129.179862090702 & 5.32013790929774 \tabularnewline
20 & 139.9 & 135.004001298371 & 4.89599870162908 \tabularnewline
21 & 159.3 & 140.049620859356 & 19.2503791406442 \tabularnewline
22 & 170.4 & 162.120760076697 & 8.27923992330312 \tabularnewline
23 & 175 & 179.733910744141 & -4.73391074414088 \tabularnewline
24 & 175.8 & 175.830177358901 & -0.0301773589012391 \tabularnewline
25 & 180.9 & 173.520238424300 & 7.37976157569961 \tabularnewline
26 & 180.3 & 176.464475796908 & 3.83552420309158 \tabularnewline
27 & 169.6 & 169.035169120544 & 0.564830879455514 \tabularnewline
28 & 172.3 & 165.515939797812 & 6.78406020218753 \tabularnewline
29 & 184.8 & 172.760905180676 & 12.0390948193237 \tabularnewline
30 & 177.7 & 173.683326023397 & 4.01667397660301 \tabularnewline
31 & 184.6 & 170.958096626394 & 13.6419033736063 \tabularnewline
32 & 211.4 & 196.644944783576 & 14.7550552164243 \tabularnewline
33 & 215.3 & 216.226327676505 & -0.926327676504908 \tabularnewline
34 & 215.9 & 222.675778177690 & -6.77577817768954 \tabularnewline
35 & 244.7 & 238.285085533756 & 6.4149144662443 \tabularnewline
36 & 259.3 & 262.184172001775 & -2.88417200177477 \tabularnewline
37 & 289 & 285.858135863837 & 3.14186413616275 \tabularnewline
38 & 310.9 & 307.580188792929 & 3.31981120707104 \tabularnewline
39 & 321 & 313.781592840499 & 7.21840715950129 \tabularnewline
40 & 315.1 & 318.489033401315 & -3.38903340131464 \tabularnewline
41 & 333.2 & 324.055412474194 & 9.14458752580584 \tabularnewline
42 & 314.1 & 321.953370901288 & -7.85337090128819 \tabularnewline
43 & 284.7 & 294.39778947713 & -9.69778947712996 \tabularnewline
44 & 273.9 & 273.339708124726 & 0.560291875273653 \tabularnewline
45 & 216 & 233.091354552643 & -17.0913545526431 \tabularnewline
46 & 196.4 & 191.227386330757 & 5.17261366924323 \tabularnewline
47 & 190.9 & 183.137268208259 & 7.76273179174125 \tabularnewline
48 & 206.4 & 193.802857061919 & 12.5971429380814 \tabularnewline
49 & 196.3 & 199.837345770043 & -3.53734577004328 \tabularnewline
50 & 199.5 & 193.604621154547 & 5.89537884545333 \tabularnewline
51 & 198.9 & 197.168799279598 & 1.73120072040208 \tabularnewline
52 & 214.4 & 213.279563465051 & 1.12043653494857 \tabularnewline
53 & 214.2 & 226.305489367921 & -12.1054893679215 \tabularnewline
54 & 187.6 & 204.396967359221 & -16.7969673592212 \tabularnewline
55 & 180.6 & 189.815574465525 & -9.21557446552495 \tabularnewline
56 & 172.2 & 188.380328792132 & -16.1803287921317 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58265&T=4

[TABLE]
[ROW][C]Multiple Linear Regression - Actuals, Interpolation, and Residuals[/C][/ROW]
[ROW][C]Time or Index[/C][C]Actuals[/C][C]InterpolationForecast[/C][C]ResidualsPrediction Error[/C][/ROW]
[ROW][C]1[/C][C]109.5[/C][C]114.208021387977[/C][C]-4.70802138797678[/C][/ROW]
[ROW][C]2[/C][C]116[/C][C]124.231763407891[/C][C]-8.23176340789096[/C][/ROW]
[ROW][C]3[/C][C]111.2[/C][C]121.675701642503[/C][C]-10.4757016425032[/C][/ROW]
[ROW][C]4[/C][C]112.1[/C][C]121.506282953435[/C][C]-9.40628295343544[/C][/ROW]
[ROW][C]5[/C][C]114[/C][C]122.560662011918[/C][C]-8.56066201191806[/C][/ROW]
[ROW][C]6[/C][C]119.1[/C][C]113.251297001668[/C][C]5.84870299833185[/C][/ROW]
[ROW][C]7[/C][C]114.1[/C][C]114.148677340249[/C][C]-0.0486773402491406[/C][/ROW]
[ROW][C]8[/C][C]115.1[/C][C]119.131017001195[/C][C]-4.03101700119537[/C][/ROW]
[ROW][C]9[/C][C]115.4[/C][C]116.632696911496[/C][C]-1.23269691149615[/C][/ROW]
[ROW][C]10[/C][C]110.8[/C][C]117.476075414857[/C][C]-6.67607541485681[/C][/ROW]
[ROW][C]11[/C][C]116[/C][C]125.443735513845[/C][C]-9.44373551384466[/C][/ROW]
[ROW][C]12[/C][C]119.2[/C][C]128.882793577405[/C][C]-9.68279357740541[/C][/ROW]
[ROW][C]13[/C][C]126.5[/C][C]128.776258553842[/C][C]-2.27625855384229[/C][/ROW]
[ROW][C]14[/C][C]127.8[/C][C]132.618950847725[/C][C]-4.81895084772499[/C][/ROW]
[ROW][C]15[/C][C]131.3[/C][C]130.338737116856[/C][C]0.961262883144313[/C][/ROW]
[ROW][C]16[/C][C]140.3[/C][C]135.409180382386[/C][C]4.89081961761399[/C][/ROW]
[ROW][C]17[/C][C]137.3[/C][C]137.81753096529[/C][C]-0.517530965289984[/C][/ROW]
[ROW][C]18[/C][C]143[/C][C]128.215038714425[/C][C]14.7849612855745[/C][/ROW]
[ROW][C]19[/C][C]134.5[/C][C]129.179862090702[/C][C]5.32013790929774[/C][/ROW]
[ROW][C]20[/C][C]139.9[/C][C]135.004001298371[/C][C]4.89599870162908[/C][/ROW]
[ROW][C]21[/C][C]159.3[/C][C]140.049620859356[/C][C]19.2503791406442[/C][/ROW]
[ROW][C]22[/C][C]170.4[/C][C]162.120760076697[/C][C]8.27923992330312[/C][/ROW]
[ROW][C]23[/C][C]175[/C][C]179.733910744141[/C][C]-4.73391074414088[/C][/ROW]
[ROW][C]24[/C][C]175.8[/C][C]175.830177358901[/C][C]-0.0301773589012391[/C][/ROW]
[ROW][C]25[/C][C]180.9[/C][C]173.520238424300[/C][C]7.37976157569961[/C][/ROW]
[ROW][C]26[/C][C]180.3[/C][C]176.464475796908[/C][C]3.83552420309158[/C][/ROW]
[ROW][C]27[/C][C]169.6[/C][C]169.035169120544[/C][C]0.564830879455514[/C][/ROW]
[ROW][C]28[/C][C]172.3[/C][C]165.515939797812[/C][C]6.78406020218753[/C][/ROW]
[ROW][C]29[/C][C]184.8[/C][C]172.760905180676[/C][C]12.0390948193237[/C][/ROW]
[ROW][C]30[/C][C]177.7[/C][C]173.683326023397[/C][C]4.01667397660301[/C][/ROW]
[ROW][C]31[/C][C]184.6[/C][C]170.958096626394[/C][C]13.6419033736063[/C][/ROW]
[ROW][C]32[/C][C]211.4[/C][C]196.644944783576[/C][C]14.7550552164243[/C][/ROW]
[ROW][C]33[/C][C]215.3[/C][C]216.226327676505[/C][C]-0.926327676504908[/C][/ROW]
[ROW][C]34[/C][C]215.9[/C][C]222.675778177690[/C][C]-6.77577817768954[/C][/ROW]
[ROW][C]35[/C][C]244.7[/C][C]238.285085533756[/C][C]6.4149144662443[/C][/ROW]
[ROW][C]36[/C][C]259.3[/C][C]262.184172001775[/C][C]-2.88417200177477[/C][/ROW]
[ROW][C]37[/C][C]289[/C][C]285.858135863837[/C][C]3.14186413616275[/C][/ROW]
[ROW][C]38[/C][C]310.9[/C][C]307.580188792929[/C][C]3.31981120707104[/C][/ROW]
[ROW][C]39[/C][C]321[/C][C]313.781592840499[/C][C]7.21840715950129[/C][/ROW]
[ROW][C]40[/C][C]315.1[/C][C]318.489033401315[/C][C]-3.38903340131464[/C][/ROW]
[ROW][C]41[/C][C]333.2[/C][C]324.055412474194[/C][C]9.14458752580584[/C][/ROW]
[ROW][C]42[/C][C]314.1[/C][C]321.953370901288[/C][C]-7.85337090128819[/C][/ROW]
[ROW][C]43[/C][C]284.7[/C][C]294.39778947713[/C][C]-9.69778947712996[/C][/ROW]
[ROW][C]44[/C][C]273.9[/C][C]273.339708124726[/C][C]0.560291875273653[/C][/ROW]
[ROW][C]45[/C][C]216[/C][C]233.091354552643[/C][C]-17.0913545526431[/C][/ROW]
[ROW][C]46[/C][C]196.4[/C][C]191.227386330757[/C][C]5.17261366924323[/C][/ROW]
[ROW][C]47[/C][C]190.9[/C][C]183.137268208259[/C][C]7.76273179174125[/C][/ROW]
[ROW][C]48[/C][C]206.4[/C][C]193.802857061919[/C][C]12.5971429380814[/C][/ROW]
[ROW][C]49[/C][C]196.3[/C][C]199.837345770043[/C][C]-3.53734577004328[/C][/ROW]
[ROW][C]50[/C][C]199.5[/C][C]193.604621154547[/C][C]5.89537884545333[/C][/ROW]
[ROW][C]51[/C][C]198.9[/C][C]197.168799279598[/C][C]1.73120072040208[/C][/ROW]
[ROW][C]52[/C][C]214.4[/C][C]213.279563465051[/C][C]1.12043653494857[/C][/ROW]
[ROW][C]53[/C][C]214.2[/C][C]226.305489367921[/C][C]-12.1054893679215[/C][/ROW]
[ROW][C]54[/C][C]187.6[/C][C]204.396967359221[/C][C]-16.7969673592212[/C][/ROW]
[ROW][C]55[/C][C]180.6[/C][C]189.815574465525[/C][C]-9.21557446552495[/C][/ROW]
[ROW][C]56[/C][C]172.2[/C][C]188.380328792132[/C][C]-16.1803287921317[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58265&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58265&T=4

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
1109.5114.208021387977-4.70802138797678
2116124.231763407891-8.23176340789096
3111.2121.675701642503-10.4757016425032
4112.1121.506282953435-9.40628295343544
5114122.560662011918-8.56066201191806
6119.1113.2512970016685.84870299833185
7114.1114.148677340249-0.0486773402491406
8115.1119.131017001195-4.03101700119537
9115.4116.632696911496-1.23269691149615
10110.8117.476075414857-6.67607541485681
11116125.443735513845-9.44373551384466
12119.2128.882793577405-9.68279357740541
13126.5128.776258553842-2.27625855384229
14127.8132.618950847725-4.81895084772499
15131.3130.3387371168560.961262883144313
16140.3135.4091803823864.89081961761399
17137.3137.81753096529-0.517530965289984
18143128.21503871442514.7849612855745
19134.5129.1798620907025.32013790929774
20139.9135.0040012983714.89599870162908
21159.3140.04962085935619.2503791406442
22170.4162.1207600766978.27923992330312
23175179.733910744141-4.73391074414088
24175.8175.830177358901-0.0301773589012391
25180.9173.5202384243007.37976157569961
26180.3176.4644757969083.83552420309158
27169.6169.0351691205440.564830879455514
28172.3165.5159397978126.78406020218753
29184.8172.76090518067612.0390948193237
30177.7173.6833260233974.01667397660301
31184.6170.95809662639413.6419033736063
32211.4196.64494478357614.7550552164243
33215.3216.226327676505-0.926327676504908
34215.9222.675778177690-6.77577817768954
35244.7238.2850855337566.4149144662443
36259.3262.184172001775-2.88417200177477
37289285.8581358638373.14186413616275
38310.9307.5801887929293.31981120707104
39321313.7815928404997.21840715950129
40315.1318.489033401315-3.38903340131464
41333.2324.0554124741949.14458752580584
42314.1321.953370901288-7.85337090128819
43284.7294.39778947713-9.69778947712996
44273.9273.3397081247260.560291875273653
45216233.091354552643-17.0913545526431
46196.4191.2273863307575.17261366924323
47190.9183.1372682082597.76273179174125
48206.4193.80285706191912.5971429380814
49196.3199.837345770043-3.53734577004328
50199.5193.6046211545475.89537884545333
51198.9197.1687992795981.73120072040208
52214.4213.2795634650511.12043653494857
53214.2226.305489367921-12.1054893679215
54187.6204.396967359221-16.7969673592212
55180.6189.815574465525-9.21557446552495
56172.2188.380328792132-16.1803287921317







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
210.2479425647432330.4958851294864660.752057435256767
220.1540354396091450.3080708792182900.845964560390855
230.1280760905211940.2561521810423870.871923909478806
240.06071249890876060.1214249978175210.93928750109124
250.05360446035325640.1072089207065130.946395539646744
260.03692833761778030.07385667523556050.96307166238222
270.08132111393939450.1626422278787890.918678886060605
280.1000833600596580.2001667201193160.899916639940342
290.1253499074377590.2506998148755180.874650092562241
300.7371002263789760.5257995472420480.262899773621024
310.6779627275523540.6440745448952920.322037272447646
320.6070998314812620.7858003370374750.392900168518738
330.6381702250388370.7236595499223260.361829774961163
340.7389991898895920.5220016202208160.261000810110408
350.7656950373123880.4686099253752250.234304962687612

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
21 & 0.247942564743233 & 0.495885129486466 & 0.752057435256767 \tabularnewline
22 & 0.154035439609145 & 0.308070879218290 & 0.845964560390855 \tabularnewline
23 & 0.128076090521194 & 0.256152181042387 & 0.871923909478806 \tabularnewline
24 & 0.0607124989087606 & 0.121424997817521 & 0.93928750109124 \tabularnewline
25 & 0.0536044603532564 & 0.107208920706513 & 0.946395539646744 \tabularnewline
26 & 0.0369283376177803 & 0.0738566752355605 & 0.96307166238222 \tabularnewline
27 & 0.0813211139393945 & 0.162642227878789 & 0.918678886060605 \tabularnewline
28 & 0.100083360059658 & 0.200166720119316 & 0.899916639940342 \tabularnewline
29 & 0.125349907437759 & 0.250699814875518 & 0.874650092562241 \tabularnewline
30 & 0.737100226378976 & 0.525799547242048 & 0.262899773621024 \tabularnewline
31 & 0.677962727552354 & 0.644074544895292 & 0.322037272447646 \tabularnewline
32 & 0.607099831481262 & 0.785800337037475 & 0.392900168518738 \tabularnewline
33 & 0.638170225038837 & 0.723659549922326 & 0.361829774961163 \tabularnewline
34 & 0.738999189889592 & 0.522001620220816 & 0.261000810110408 \tabularnewline
35 & 0.765695037312388 & 0.468609925375225 & 0.234304962687612 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58265&T=5

[TABLE]
[ROW][C]Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]p-values[/C][C]Alternative Hypothesis[/C][/ROW]
[ROW][C]breakpoint index[/C][C]greater[/C][C]2-sided[/C][C]less[/C][/ROW]
[ROW][C]21[/C][C]0.247942564743233[/C][C]0.495885129486466[/C][C]0.752057435256767[/C][/ROW]
[ROW][C]22[/C][C]0.154035439609145[/C][C]0.308070879218290[/C][C]0.845964560390855[/C][/ROW]
[ROW][C]23[/C][C]0.128076090521194[/C][C]0.256152181042387[/C][C]0.871923909478806[/C][/ROW]
[ROW][C]24[/C][C]0.0607124989087606[/C][C]0.121424997817521[/C][C]0.93928750109124[/C][/ROW]
[ROW][C]25[/C][C]0.0536044603532564[/C][C]0.107208920706513[/C][C]0.946395539646744[/C][/ROW]
[ROW][C]26[/C][C]0.0369283376177803[/C][C]0.0738566752355605[/C][C]0.96307166238222[/C][/ROW]
[ROW][C]27[/C][C]0.0813211139393945[/C][C]0.162642227878789[/C][C]0.918678886060605[/C][/ROW]
[ROW][C]28[/C][C]0.100083360059658[/C][C]0.200166720119316[/C][C]0.899916639940342[/C][/ROW]
[ROW][C]29[/C][C]0.125349907437759[/C][C]0.250699814875518[/C][C]0.874650092562241[/C][/ROW]
[ROW][C]30[/C][C]0.737100226378976[/C][C]0.525799547242048[/C][C]0.262899773621024[/C][/ROW]
[ROW][C]31[/C][C]0.677962727552354[/C][C]0.644074544895292[/C][C]0.322037272447646[/C][/ROW]
[ROW][C]32[/C][C]0.607099831481262[/C][C]0.785800337037475[/C][C]0.392900168518738[/C][/ROW]
[ROW][C]33[/C][C]0.638170225038837[/C][C]0.723659549922326[/C][C]0.361829774961163[/C][/ROW]
[ROW][C]34[/C][C]0.738999189889592[/C][C]0.522001620220816[/C][C]0.261000810110408[/C][/ROW]
[ROW][C]35[/C][C]0.765695037312388[/C][C]0.468609925375225[/C][C]0.234304962687612[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58265&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58265&T=5

As an alternative you can also use a QR Code:  

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

Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
210.2479425647432330.4958851294864660.752057435256767
220.1540354396091450.3080708792182900.845964560390855
230.1280760905211940.2561521810423870.871923909478806
240.06071249890876060.1214249978175210.93928750109124
250.05360446035325640.1072089207065130.946395539646744
260.03692833761778030.07385667523556050.96307166238222
270.08132111393939450.1626422278787890.918678886060605
280.1000833600596580.2001667201193160.899916639940342
290.1253499074377590.2506998148755180.874650092562241
300.7371002263789760.5257995472420480.262899773621024
310.6779627275523540.6440745448952920.322037272447646
320.6070998314812620.7858003370374750.392900168518738
330.6381702250388370.7236595499223260.361829774961163
340.7389991898895920.5220016202208160.261000810110408
350.7656950373123880.4686099253752250.234304962687612







Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level00OK
10% type I error level10.0666666666666667OK

\begin{tabular}{lllllllll}
\hline
Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
Description & # significant tests & % significant tests & OK/NOK \tabularnewline
1% type I error level & 0 & 0 & OK \tabularnewline
5% type I error level & 0 & 0 & OK \tabularnewline
10% type I error level & 1 & 0.0666666666666667 & OK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58265&T=6

[TABLE]
[ROW][C]Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]Description[/C][C]# significant tests[/C][C]% significant tests[/C][C]OK/NOK[/C][/ROW]
[ROW][C]1% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]1[/C][C]0.0666666666666667[/C][C]OK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58265&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58265&T=6

As an alternative you can also use a QR Code:  

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

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level00OK
10% type I error level10.0666666666666667OK



Parameters (Session):
par1 = 1 ; par2 = Include Monthly Dummies ; par3 = Linear Trend ;
Parameters (R input):
par1 = 1 ; par2 = Include Monthly Dummies ; par3 = Linear Trend ;
R code (references can be found in the software module):
library(lattice)
library(lmtest)
n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
par1 <- as.numeric(par1)
x <- 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'){
x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep='')))
for (i in 1:n-1) {
for (j in 1:k) {
x2[i,j] <- x[i+1,j] - x[i,j]
}
}
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[1,])
if (par3 == 'Linear Trend'){
x <- cbind(x, c(1:n))
colnames(x)[k+1] <- 't'
}
x
k <- length(x[1,])
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$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
grid()
dev.off()
bitmap(file='test2.png')
hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
grid()
dev.off()
bitmap(file='test3.png')
densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
dev.off()
bitmap(file='test4.png')
qqnorm(mysum$resid, main='Residual Normal Q-Q Plot')
qqline(mysum$resid)
grid()
dev.off()
(myerror <- as.ts(mysum$resid))
bitmap(file='test5.png')
dum <- cbind(lag(myerror,k=1),myerror)
dum
dum1 <- dum[2:length(myerror),]
dum1
z <- as.data.frame(dum1)
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$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
myeq <- paste(myeq, mysum$coefficients[i,1], sep=' ')
if (rownames(mysum$coefficients)[i] != '(Intercept)') {
myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='')
if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
}
}
myeq <- paste(myeq, ' + e[t]')
a<-table.row.start(a)
a<-table.element(a, myeq)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable1.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,hyperlink('ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Variable',header=TRUE)
a<-table.element(a,'Parameter',header=TRUE)
a<-table.element(a,'S.D.',header=TRUE)
a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
a<-table.element(a,'2-tail p-value',header=TRUE)
a<-table.element(a,'1-tail p-value',header=TRUE)
a<-table.row.end(a)
for (i in 1:k){
a<-table.row.start(a)
a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE)
a<-table.element(a,mysum$coefficients[i,1])
a<-table.element(a, round(mysum$coefficients[i,2],6))
a<-table.element(a, round(mysum$coefficients[i,3],4))
a<-table.element(a, round(mysum$coefficients[i,4],6))
a<-table.element(a, round(mysum$coefficients[i,4]/2,6))
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable2.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple R',1,TRUE)
a<-table.element(a, sqrt(mysum$r.squared))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'R-squared',1,TRUE)
a<-table.element(a, mysum$r.squared)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Adjusted R-squared',1,TRUE)
a<-table.element(a, mysum$adj.r.squared)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (value)',1,TRUE)
a<-table.element(a, mysum$fstatistic[1])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
a<-table.element(a, mysum$fstatistic[2])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE)
a<-table.element(a, mysum$fstatistic[3])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'p-value',1,TRUE)
a<-table.element(a, 1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]))
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, mysum$sigma)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
a<-table.element(a, sum(myerror*myerror))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable3.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Time or Index', 1, TRUE)
a<-table.element(a, 'Actuals', 1, TRUE)
a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
a<-table.row.end(a)
for (i in 1:n) {
a<-table.row.start(a)
a<-table.element(a,i, 1, TRUE)
a<-table.element(a,x[i])
a<-table.element(a,x[i]-mysum$resid[i])
a<-table.element(a,mysum$resid[i])
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,gqarr[mypoint-kp3+1,1])
a<-table.element(a,gqarr[mypoint-kp3+1,2])
a<-table.element(a,gqarr[mypoint-kp3+1,3])
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,numsignificant1)
a<-table.element(a,numsignificant1/numgqtests)
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,numsignificant5)
a<-table.element(a,numsignificant5/numgqtests)
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,numsignificant10)
a<-table.element(a,numsignificant10/numgqtests)
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')
}