FreeStatistics.org

Free Statistics

of Irreproducible Research!

Description of Statistical Computation

Author's title

Author*The author of this computation has been verified*
R Software Moduleesteq.wasp
Title produced by softwareEstimate Equation
Date of computationTue, 16 Nov 2010 10:02:22 +0000
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2010/Nov/16/t1289902594munww6jrvcyfs76.htm/, Retrieved Sat, 04 May 2024 20:37:56 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=95337, Retrieved Sat, 04 May 2024 20:37:56 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact179

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Linear Regression Graphical Model Validation] [Colombia Coffee -...] [2008-02-26 10:22:06] [74be16979710d4c4e7c6647856088456]
-  M D  [Linear Regression Graphical Model Validation] [workshop 6 tutorial] [2010-11-12 10:13:29] [87d60b8864dc39f7ed759c345edfb471]
-    D    [Linear Regression Graphical Model Validation] [workshop 6 mini-t...] [2010-11-12 14:05:27] [87d60b8864dc39f7ed759c345edfb471]
F RMPD        [Estimate Equation] [] [2010-11-16 10:02:22] [6c31f786e793d35ef3a03978bc5de774] [Current]
Feedback Forum
2010-11-20 16:57:16 [48eb36e2c01435ad7e4ea7854a9d98fe] [reply] 
Uit de omschrijving van de hypothese en van de beschikbare gegevens blijkt dat student enkelvoudige lineaire regressie wil onderzoeken en hiervoor een model wil opstellen. Echter de softwaremodule die werd gebruikt is deze voor meervoudige regressie. Beide vaststellingen zijn dus contradictorisch.

Post a new message

Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
15	15
14	0
10	3
12	2
10	3
9	12
9	3
18	0
14	15
15	20
14	2
24	3
9	4
18	2
14	4
13	0
18	15
12	9
13	1
12	5
5	4
9	15
11	4
11	12
16	4
14	2
8	4
18	30
10	6
13	7
12	17
12	5
12	0
13	3
7	0
14	
9	11
9	10
13	0
10	0
10	0
11	0
13	0
6	0
21	12
11	6
9	12
18	10
9	0
15	16
11	2
14	0
8	0
14	1
8	14
11	0
20	3
8	0
13	12
13	15
15	8
12	6
12	5
21	10
24	4
12	8
17	20
11	0
15	0
12	10
14	6
12	16
20	6
12	4
10	9
11	17
19	0
16	4
15	16
14	20

Tables (Output of Computation)





Multiple Linear Regression - Estimated Regression Equation
Sport[t] = +0.26386755419013 Verwachting[t] +3.1329151732378 + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Sport[t] = +0.26386755419013 Verwachting[t] +3.1329151732378 + e[t] \tabularnewline \hline \end{tabular} %Source: https://freestatistics.org/blog/index.php?pk=95337&T=0

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW]
Sport[t] = +0.26386755419013 Verwachting[t] +3.1329151732378 + e[t][/C][/ROW] [/TABLE] Source: https://freestatistics.org/blog/index.php?pk=95337&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=95337&T=0

As an alternative you can also use a QR Code:  
QR Code


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

Multiple Linear Regression - Estimated Regression Equation
Sport[t] = +0.26386755419013 Verwachting[t] +3.1329151732378 + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.E.T-STATH0: parameter = 02-tail p-value1-tail p-value
Verwachting[t]0.2638680.1902491.3869580.1694050.084703
Constant3.1329152.5690671.2194760.2263380.113169
VariableElasticityS.E.*T-STATH0: |elast| = 12-tail p-value1-tail p-value
%Verwachting[t]0.5216920.376141-1.2716170.2072880.103644
%Constant0.4783080.392224-1.3300880.1873660.093683
VariableStand. Coeff.S.E.*T-STATH0: coeff = 02-tail p-value1-tail p-value
S-Verwachting[t]0.1551410.1118571.3869580.1694050.084703
S-Constant00010.5
*Notecomputed against deterministic endogenous series
VariablePartial Correlation
Verwachting[t]0.155141
Constant0.136781
Critical Values (alpha = 5%)
1-tail CV at 5%1.65
2-tail CV at 5%1.96

\begin{tabular}{lllllllll}
\hline

Multiple Linear Regression - Ordinary Least Squares \tabularnewline
VariableParameterS.E.T-STATH0: parameter = 02-tail p-value1-tail p-value \tabularnewline Verwachting[t]0.2638680.1902491.3869580.1694050.084703 \tabularnewline Constant3.1329152.5690671.2194760.2263380.113169 \tabularnewline \tabularnewline VariableElasticityS.E.*T-STATH0: |elast| = 12-tail p-value1-tail p-value \tabularnewline %Verwachting[t]0.5216920.376141-1.2716170.2072880.103644 \tabularnewline %Constant0.4783080.392224-1.3300880.1873660.093683 \tabularnewline VariableStand. Coeff.S.E.*T-STATH0: coeff = 02-tail p-value1-tail p-value \tabularnewline S-Verwachting[t]0.1551410.1118571.3869580.1694050.084703 \tabularnewline S-Constant00010.5 \tabularnewline *Notecomputed against deterministic endogenous series \tabularnewline VariablePartial Correlation \tabularnewline Verwachting[t]0.155141 \tabularnewline Constant0.136781 \tabularnewline Critical Values (alpha = 5%) \tabularnewline 1-tail CV at 5%1.65 \tabularnewline 2-tail CV at 5%1.96 \tabularnewline \hline \end{tabular} %Source: https://freestatistics.org/blog/index.php?pk=95337&T=1

[TABLE]

[ROW][C]Multiple Linear Regression - Ordinary Least Squares[/C][/ROW]

[ROW]
Variable[/C]Parameter[/C]S.E.[/C]T-STATH0: parameter = 0[/C]2-tail p-value[/C]1-tail p-value[/C][/ROW] [ROW][C]Verwachting[t][/C]0.263868[/C]0.190249[/C]1.386958[/C]0.169405[/C]0.084703[/C][/ROW] [ROW][C]Constant[/C]3.132915[/C]2.569067[/C]1.219476[/C]0.226338[/C]0.113169[/C][/ROW] [ROW][C][/C][/ROW] [ROW]Variable[/C]Elasticity[/C]S.E.*[/C]T-STATH0: |elast| = 1[/C]2-tail p-value[/C]1-tail p-value[/C][/ROW] [ROW][C]%Verwachting[t][/C]0.521692[/C]0.376141[/C]-1.271617[/C]0.207288[/C]0.103644[/C][/ROW] [ROW][C]%Constant[/C]0.478308[/C]0.392224[/C]-1.330088[/C]0.187366[/C]0.093683[/C][/ROW] [ROW]Variable[/C]Stand. Coeff.[/C]S.E.*[/C]T-STATH0: coeff = 0[/C]2-tail p-value[/C]1-tail p-value[/C][/ROW] [ROW][C]S-Verwachting[t][/C]0.155141[/C]0.111857[/C]1.386958[/C]0.169405[/C]0.084703[/C][/ROW] [ROW][C]S-Constant[/C]0[/C]0[/C]0[/C]1[/C]0.5[/C][/ROW] [ROW][C]*Note[/C]computed against deterministic endogenous series[/C][/ROW] [ROW]Variable[/C]Partial Correlation[/C][/ROW] [ROW][C]Verwachting[t][/C]0.155141[/C][/ROW] [ROW][C]Constant[/C]0.136781[/C][/ROW] [ROW][C]Critical Values (alpha = 5%)[/C][/ROW] [ROW][C]1-tail CV at 5%[/C]1.65[/C][/ROW] [ROW][C]2-tail CV at 5%[/C]1.96[/C][/ROW] [/TABLE] Source: https://freestatistics.org/blog/index.php?pk=95337&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=95337&T=1

As an alternative you can also use a QR Code:  
QR Code


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

Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.E.T-STATH0: parameter = 02-tail p-value1-tail p-value
Verwachting[t]0.2638680.1902491.3869580.1694050.084703
Constant3.1329152.5690671.2194760.2263380.113169
VariableElasticityS.E.*T-STATH0: |elast| = 12-tail p-value1-tail p-value
%Verwachting[t]0.5216920.376141-1.2716170.2072880.103644
%Constant0.4783080.392224-1.3300880.1873660.093683
VariableStand. Coeff.S.E.*T-STATH0: coeff = 02-tail p-value1-tail p-value
S-Verwachting[t]0.1551410.1118571.3869580.1694050.084703
S-Constant00010.5
*Notecomputed against deterministic endogenous series
VariablePartial Correlation
Verwachting[t]0.155141
Constant0.136781
Critical Values (alpha = 5%)
1-tail CV at 5%1.65
2-tail CV at 5%1.96






Multiple Linear Regression - Regression Statistics
Multiple R0.155141
R-squared0.024069
Adjusted R-squared0.011557
F-TEST1.923653
Observations80
Degrees of Freedom78
Multiple Linear Regression - Residual Statistics
Standard Error6.512525
Sum Squared Errors3308.212152
Log Likelihood-262.400545
Durbin-Watson1.867444
Von Neumann Ratio1.891082
# e[t] > 031
# e[t] < 049
# Runs37
Stand. Normal Runs Statistic-0.468466

\begin{tabular}{lllllllll}
\hline

Multiple Linear Regression - Regression Statistics \tabularnewline

Multiple R
0.155141 \tabularnewline R-squared0.024069 \tabularnewline Adjusted R-squared0.011557 \tabularnewline F-TEST1.923653 \tabularnewline Observations80 \tabularnewline Degrees of Freedom78 \tabularnewline Multiple Linear Regression - Residual Statistics \tabularnewline Standard Error6.512525 \tabularnewline Sum Squared Errors3308.212152 \tabularnewline Log Likelihood-262.400545 \tabularnewline Durbin-Watson1.867444 \tabularnewline Von Neumann Ratio1.891082 \tabularnewline # e[t] > 031 \tabularnewline # e[t] < 049 \tabularnewline # Runs37 \tabularnewline Stand. Normal Runs Statistic-0.468466 \tabularnewline \hline \end{tabular} %Source: https://freestatistics.org/blog/index.php?pk=95337&T=2

[TABLE]

[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]

[ROW][C]Multiple R[/C]
0.155141[/C][/ROW] [ROW][C]R-squared[/C]0.024069[/C][/ROW] [ROW][C]Adjusted R-squared[/C]0.011557[/C][/ROW] [ROW][C]F-TEST[/C]1.923653[/C][/ROW] [ROW][C]Observations[/C]80[/C][/ROW] [ROW][C]Degrees of Freedom[/C]78[/C][/ROW] [ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW] [ROW][C]Standard Error[/C]6.512525[/C][/ROW] [ROW][C]Sum Squared Errors[/C]3308.212152[/C][/ROW] [ROW][C]Log Likelihood[/C]-262.400545[/C][/ROW] [ROW][C]Durbin-Watson[/C]1.867444[/C][/ROW] [ROW][C]Von Neumann Ratio[/C]1.891082[/C][/ROW] [ROW][C]# e[t] > 0[/C]31[/C][/ROW] [ROW][C]# e[t] < 0[/C]49[/C][/ROW] [ROW][C]# Runs[/C]37[/C][/ROW] [ROW][C]Stand. Normal Runs Statistic[/C]-0.468466[/C][/ROW] [/TABLE] Source: https://freestatistics.org/blog/index.php?pk=95337&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=95337&T=2

As an alternative you can also use a QR Code:  
QR Code


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

Multiple Linear Regression - Regression Statistics
Multiple R0.155141
R-squared0.024069
Adjusted R-squared0.011557
F-TEST1.923653
Observations80
Degrees of Freedom78
Multiple Linear Regression - Residual Statistics
Standard Error6.512525
Sum Squared Errors3308.212152
Log Likelihood-262.400545
Durbin-Watson1.867444
Von Neumann Ratio1.891082
# e[t] > 031
# e[t] < 049
# Runs37
Stand. Normal Runs Statistic-0.468466






Multiple Linear Regression - Ad Hoc Selection Test Statistics
Akaike (1969) Final Prediction Error43.473301
Akaike (1973) Log Information Criterion3.772137
Akaike (1974) Information Criterion43.472848
Schwarz (1978) Log Criterion3.831687
Schwarz (1978) Criterion46.140321
Craven-Wahba (1979) Generalized Cross Validation43.500489
Hannan-Quinn (1979) Criterion44.523276
Rice (1984) Criterion43.529107
Shibata (1981) Criterion43.420284

\begin{tabular}{lllllllll}
\hline

Multiple Linear Regression - Ad Hoc Selection Test Statistics \tabularnewline

Akaike (1969) Final Prediction Error
43.473301 \tabularnewline Akaike (1973) Log Information Criterion3.772137 \tabularnewline Akaike (1974) Information Criterion43.472848 \tabularnewline Schwarz (1978) Log Criterion3.831687 \tabularnewline Schwarz (1978) Criterion46.140321 \tabularnewline Craven-Wahba (1979) Generalized Cross Validation43.500489 \tabularnewline Hannan-Quinn (1979) Criterion44.523276 \tabularnewline Rice (1984) Criterion43.529107 \tabularnewline Shibata (1981) Criterion43.420284 \tabularnewline \hline \end{tabular} %Source: https://freestatistics.org/blog/index.php?pk=95337&T=3

[TABLE]

[ROW][C]Multiple Linear Regression - Ad Hoc Selection Test Statistics[/C][/ROW]

[ROW][C]Akaike (1969) Final Prediction Error[/C]
43.473301[/C][/ROW] [ROW][C]Akaike (1973) Log Information Criterion[/C]3.772137[/C][/ROW] [ROW][C]Akaike (1974) Information Criterion[/C]43.472848[/C][/ROW] [ROW][C]Schwarz (1978) Log Criterion[/C]3.831687[/C][/ROW] [ROW][C]Schwarz (1978) Criterion[/C]46.140321[/C][/ROW] [ROW][C]Craven-Wahba (1979) Generalized Cross Validation[/C]43.500489[/C][/ROW] [ROW][C]Hannan-Quinn (1979) Criterion[/C]44.523276[/C][/ROW] [ROW][C]Rice (1984) Criterion[/C]43.529107[/C][/ROW] [ROW][C]Shibata (1981) Criterion[/C]43.420284[/C][/ROW] [/TABLE] Source: https://freestatistics.org/blog/index.php?pk=95337&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=95337&T=3

As an alternative you can also use a QR Code:  
QR Code


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

Multiple Linear Regression - Ad Hoc Selection Test Statistics
Akaike (1969) Final Prediction Error43.473301
Akaike (1973) Log Information Criterion3.772137
Akaike (1974) Information Criterion43.472848
Schwarz (1978) Log Criterion3.831687
Schwarz (1978) Criterion46.140321
Craven-Wahba (1979) Generalized Cross Validation43.500489
Hannan-Quinn (1979) Criterion44.523276
Rice (1984) Criterion43.529107
Shibata (1981) Criterion43.420284






Multiple Linear Regression - Analysis of Variance
ANOVADFSum of SquaresMean Square
Regression181.58784881.587848
Residual783308.21215242.412976
Total793389.842.908860759494
F-TEST1.923653
p-value0.169405

\begin{tabular}{lllllllll}
\hline

Multiple Linear Regression - Analysis of Variance \tabularnewline

ANOVA & DF & Sum of Squares & Mean Square \tabularnewline

Regression
181.58784881.587848 \tabularnewline Residual783308.21215242.412976 \tabularnewline Total793389.842.908860759494 \tabularnewline F-TEST1.923653 \tabularnewline p-value0.169405 \tabularnewline
\hline \end{tabular} %Source: https://freestatistics.org/blog/index.php?pk=95337&T=4

[TABLE]

[ROW][C]Multiple Linear Regression - Analysis of Variance[/C][/ROW]

[ROW][C]ANOVA[/C][C]DF[/C][C]Sum of Squares[/C][C]Mean Square[/C][/ROW]

[ROW][C]Regression[/C]
1[/C]81.587848[/C]81.587848[/C][/ROW] [ROW][C]Residual[/C]78[/C]3308.212152[/C]42.412976[/C][/ROW] [ROW][C]Total[/C]79[/C]3389.8[/C]42.908860759494[/C][/ROW] [ROW][C]F-TEST[/C]1.923653[/C][/ROW] [ROW][C]p-value[/C]0.169405[/C][/ROW]
[/TABLE] Source: https://freestatistics.org/blog/index.php?pk=95337&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=95337&T=4

As an alternative you can also use a QR Code:  
QR Code


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

Multiple Linear Regression - Analysis of Variance
ANOVADFSum of SquaresMean Square
Regression181.58784881.587848
Residual783308.21215242.412976
Total793389.842.908860759494
F-TEST1.923653
p-value0.169405


Figures (Output of Computation)

Input Parameters & R Code

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