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Description of Statistical Computation

Author's title

Author*Unverified author*
R Software Moduleesteq.wasp
Title produced by softwareEstimate Equation
Date of computationThu, 13 Mar 2008 16:59:23 -0600
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2008/Mar/14/t12054496826a8cift7ssmr45x.htm/, Retrieved Sat, 18 May 2024 09:37:05 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=10070, Retrieved Sat, 18 May 2024 09:37:05 +0000
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Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact342

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Estimate Equation] [mincer's equation...] [2008-03-13 22:59:23] [d41d8cd98f00b204e9800998ecf8427e] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
12	30	900	52000
14	23	529	16100
14	31	961	50000
12	33	1089	41700
14	1	1	5800
18	24	576	30000
18	20	400	35000
12	28	784	18400
14	30	900	12500
14	40	1600	324000
9	7	49	26600
12	46	2116	21800
14	23	529	13000
11	3	9	4000
14	13	169	2400
12	16	256	40000
12	16	256	11500
11	17	289	120000
14	21	441	4800
12	26	676	2100
10	6	36	25000
14	22	484	56000
14	13	169	12000
11	1	1	800
14	26	676	20000
14	6	36	23000
14	23	529	1800
18	38	1444	60000
12	27	729	22100
16	25	625	16000
14	31	961	67000
14	-2	4	5000
14	29	841	57000
11	1	1	2000
14	5	25	35000
18	32	1024	324000
20	22	484	125000
	34	1156	18900
12	33	1089	16000
12	33	1089	62000
18	29	841	45000
16	22	484	27500
14	19	361	41000
14	13	169	3000
12	7	49	16000
14	11	121	15000
9	36	1296	25000
16	15	225	36000
14	64	4096	28200
14	17	289	36000
16	46	2116	130000
14	24	576	36000
12	10	100	12000
16	24	576	22000
11	0	0	5000
18	24	576	30200
12	13	169	14400
14	12	144	24800
12	40	1600	57000
12	40	1600	22000
9	35	1225	7000
16	23	529	23500
12	2	4	6600
12	1	1	20200
14	30	900	36500
14	26	676	21600
16	31	961	100000
12	30	900	9700
10	38	1444	10000
14	10	100	39100
16	35	1225	20400
12	1	1	4900
9	37	1369	100000
14	19	361	35000
14	20	400	10000
14	35	1225	37000
14	14	196	8000
14	23	529	10000
14	25	625	6000
12	13	169	30000
12	6	36	10200
12	1	1	6000
12	22	484	8000
16	12	144	26200
12	15	225	30000
20	54	2916	8000
16	24	576	34000
16	31	961	27500
10	4	16	500
12	6	36	35000
12	1	1	2400
11	1	1	6100
12	3	9	9000
14	37	1369	74000
20	22	484	31000
14	7	49	6600
11	21	441	16800
12	26	676	28700
12	17	289	12000
14	16	256	10600
16	5	25	4800
14	2	4	12000
14	3	9	10000
9	45	2025	6000
9	28	784	84000
14	6	36	29900
12	53	2809	324000
12	16	256	12000
10	29	841	2900
20	23	529	100000
14	9	81	35000
20	32	1024	88000
20	30	900	64000
12	23	529	38000
14	37	1369	58000
14	21	441	12000
16	1	1	21000
14	20	400	50000
12	25	625	11000
14	26	676	9100
16	6	36	27900
14	24	576	30000
14	21	441	7100
14	28	784	45000
14	1	1	1200
14	32	1024	20700
12	44	1936	40400
12	12	144	118000
14	39	1521	57000
16	46	2116	25500
16	20	400	80000
14	22	484	40000
14	4	16	8500
12	20	400	26000
11	22	484	26000
16	27	729	10900
14	25	625	46300
18	27	729	28200
14	5	25	21000
12	38	1444	38000
11	3	9	10000

Tables (Output of Computation)





Multiple Linear Regression - Estimated Regression Equation
ln(incwage[t]) = +0.088577332312901 school[t] +0.072846046324836 workexp[t] -0.00076969626073926 work2[t] +7.6307365818243 + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
ln(incwage[t]) = +0.088577332312901 school[t] +0.072846046324836 workexp[t] -0.00076969626073926 work2[t] +7.6307365818243 + e[t] \tabularnewline \hline \end{tabular} %Source: https://freestatistics.org/blog/index.php?pk=10070&T=0

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW]
ln(incwage[t]) = +0.088577332312901 school[t] +0.072846046324836 workexp[t] -0.00076969626073926 work2[t] +7.6307365818243 + e[t][/C][/ROW] [/TABLE] Source: https://freestatistics.org/blog/index.php?pk=10070&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=10070&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
ln(incwage[t]) = +0.088577332312901 school[t] +0.072846046324836 workexp[t] -0.00076969626073926 work2[t] +7.6307365818243 + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.E.T-STATH0: parameter = 02-tail p-value1-tail p-value
school[t]0.0885770.0306092.893870.0044290.002214
workexp[t]0.0728460.0173534.1979864.8E-52.4E-5
work2[t]-0.000770.00035-2.1973680.0296740.014837
Constant7.6307370.43284417.62930500
VariableElasticityS.E.*T-STATH0: |elast| = 12-tail p-value1-tail p-value
%school[t]0.1213260.041925-20.95815200
%workexp[t]0.1562730.037226-22.66521800
%work2[t]-0.0487790.022199-42.84978200
%Constant0.7711810.043744-5.2308471.0E-60
VariableStand. Coeff.S.E.*T-STATH0: coeff = 02-tail p-value1-tail p-value
S-school[t]0.2107180.0728152.893870.0044290.002214
S-workexp[t]0.8615610.2052324.1979864.8E-52.4E-5
S-work2[t]-0.4491910.204422-2.1973680.0296740.014837
S-Constant00010.5
*Notecomputed against deterministic endogenous series
VariablePartial Correlation
school[t]0.240013
workexp[t]0.337601
work2[t]-0.184511
Constant0.833099
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 school[t]0.0885770.0306092.893870.0044290.002214 \tabularnewline workexp[t]0.0728460.0173534.1979864.8E-52.4E-5 \tabularnewline work2[t]-0.000770.00035-2.1973680.0296740.014837 \tabularnewline Constant7.6307370.43284417.62930500 \tabularnewline \tabularnewline VariableElasticityS.E.*T-STATH0: |elast| = 12-tail p-value1-tail p-value \tabularnewline %school[t]0.1213260.041925-20.95815200 \tabularnewline %workexp[t]0.1562730.037226-22.66521800 \tabularnewline %work2[t]-0.0487790.022199-42.84978200 \tabularnewline %Constant0.7711810.043744-5.2308471.0E-60 \tabularnewline VariableStand. Coeff.S.E.*T-STATH0: coeff = 02-tail p-value1-tail p-value \tabularnewline S-school[t]0.2107180.0728152.893870.0044290.002214 \tabularnewline S-workexp[t]0.8615610.2052324.1979864.8E-52.4E-5 \tabularnewline S-work2[t]-0.4491910.204422-2.1973680.0296740.014837 \tabularnewline S-Constant00010.5 \tabularnewline *Notecomputed against deterministic endogenous series \tabularnewline VariablePartial Correlation \tabularnewline school[t]0.240013 \tabularnewline workexp[t]0.337601 \tabularnewline work2[t]-0.184511 \tabularnewline Constant0.833099 \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=10070&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]school[t][/C]0.088577[/C]0.030609[/C]2.89387[/C]0.004429[/C]0.002214[/C][/ROW] [ROW][C]workexp[t][/C]0.072846[/C]0.017353[/C]4.197986[/C]4.8E-5[/C]2.4E-5[/C][/ROW] [ROW][C]work2[t][/C]-0.00077[/C]0.00035[/C]-2.197368[/C]0.029674[/C]0.014837[/C][/ROW] [ROW][C]Constant[/C]7.630737[/C]0.432844[/C]17.629305[/C]0[/C]0[/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]%school[t][/C]0.121326[/C]0.041925[/C]-20.958152[/C]0[/C]0[/C][/ROW] [ROW][C]%workexp[t][/C]0.156273[/C]0.037226[/C]-22.665218[/C]0[/C]0[/C][/ROW] [ROW][C]%work2[t][/C]-0.048779[/C]0.022199[/C]-42.849782[/C]0[/C]0[/C][/ROW] [ROW][C]%Constant[/C]0.771181[/C]0.043744[/C]-5.230847[/C]1.0E-6[/C]0[/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-school[t][/C]0.210718[/C]0.072815[/C]2.89387[/C]0.004429[/C]0.002214[/C][/ROW] [ROW][C]S-workexp[t][/C]0.861561[/C]0.205232[/C]4.197986[/C]4.8E-5[/C]2.4E-5[/C][/ROW] [ROW][C]S-work2[t][/C]-0.449191[/C]0.204422[/C]-2.197368[/C]0.029674[/C]0.014837[/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]school[t][/C]0.240013[/C][/ROW] [ROW][C]workexp[t][/C]0.337601[/C][/ROW] [ROW][C]work2[t][/C]-0.184511[/C][/ROW] [ROW][C]Constant[/C]0.833099[/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=10070&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=10070&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
school[t]0.0885770.0306092.893870.0044290.002214
workexp[t]0.0728460.0173534.1979864.8E-52.4E-5
work2[t]-0.000770.00035-2.1973680.0296740.014837
Constant7.6307370.43284417.62930500
VariableElasticityS.E.*T-STATH0: |elast| = 12-tail p-value1-tail p-value
%school[t]0.1213260.041925-20.95815200
%workexp[t]0.1562730.037226-22.66521800
%work2[t]-0.0487790.022199-42.84978200
%Constant0.7711810.043744-5.2308471.0E-60
VariableStand. Coeff.S.E.*T-STATH0: coeff = 02-tail p-value1-tail p-value
S-school[t]0.2107180.0728152.893870.0044290.002214
S-workexp[t]0.8615610.2052324.1979864.8E-52.4E-5
S-work2[t]-0.4491910.204422-2.1973680.0296740.014837
S-Constant00010.5
*Notecomputed against deterministic endogenous series
VariablePartial Correlation
school[t]0.240013
workexp[t]0.337601
work2[t]-0.184511
Constant0.833099
Critical Values (alpha = 5%)
1-tail CV at 5%1.65
2-tail CV at 5%1.96






Multiple Linear Regression - Regression Statistics
Multiple R0.540472
R-squared0.29211
Adjusted R-squared0.276609
F-TEST18.844296
Observations141
Degrees of Freedom137
Multiple Linear Regression - Residual Statistics
Standard Error0.958797
Sum Squared Errors125.942868
Log Likelihood-192.108662
Durbin-Watson2.134577
Von Neumann Ratio2.149824
# e[t] > 076
# e[t] < 065
# Runs77
Stand. Normal Runs Statistic1.008404

\begin{tabular}{lllllllll}
\hline

Multiple Linear Regression - Regression Statistics \tabularnewline

Multiple R
0.540472 \tabularnewline R-squared0.29211 \tabularnewline Adjusted R-squared0.276609 \tabularnewline F-TEST18.844296 \tabularnewline Observations141 \tabularnewline Degrees of Freedom137 \tabularnewline Multiple Linear Regression - Residual Statistics \tabularnewline Standard Error0.958797 \tabularnewline Sum Squared Errors125.942868 \tabularnewline Log Likelihood-192.108662 \tabularnewline Durbin-Watson2.134577 \tabularnewline Von Neumann Ratio2.149824 \tabularnewline # e[t] > 076 \tabularnewline # e[t] < 065 \tabularnewline # Runs77 \tabularnewline Stand. Normal Runs Statistic1.008404 \tabularnewline \hline \end{tabular} %Source: https://freestatistics.org/blog/index.php?pk=10070&T=2

[TABLE]

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

[ROW][C]Multiple R[/C]
0.540472[/C][/ROW] [ROW][C]R-squared[/C]0.29211[/C][/ROW] [ROW][C]Adjusted R-squared[/C]0.276609[/C][/ROW] [ROW][C]F-TEST[/C]18.844296[/C][/ROW] [ROW][C]Observations[/C]141[/C][/ROW] [ROW][C]Degrees of Freedom[/C]137[/C][/ROW] [ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW] [ROW][C]Standard Error[/C]0.958797[/C][/ROW] [ROW][C]Sum Squared Errors[/C]125.942868[/C][/ROW] [ROW][C]Log Likelihood[/C]-192.108662[/C][/ROW] [ROW][C]Durbin-Watson[/C]2.134577[/C][/ROW] [ROW][C]Von Neumann Ratio[/C]2.149824[/C][/ROW] [ROW][C]# e[t] > 0[/C]76[/C][/ROW] [ROW][C]# e[t] < 0[/C]65[/C][/ROW] [ROW][C]# Runs[/C]77[/C][/ROW] [ROW][C]Stand. Normal Runs Statistic[/C]1.008404[/C][/ROW] [/TABLE] Source: https://freestatistics.org/blog/index.php?pk=10070&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=10070&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 R0.540472
R-squared0.29211
Adjusted R-squared0.276609
F-TEST18.844296
Observations141
Degrees of Freedom137
Multiple Linear Regression - Residual Statistics
Standard Error0.958797
Sum Squared Errors125.942868
Log Likelihood-192.108662
Durbin-Watson2.134577
Von Neumann Ratio2.149824
# e[t] > 076
# e[t] < 065
# Runs77
Stand. Normal Runs Statistic1.008404






Multiple Linear Regression - Ad Hoc Selection Test Statistics
Akaike (1969) Final Prediction Error0.94537
Akaike (1973) Log Information Criterion-0.056194
Akaike (1974) Information Criterion0.945356
Schwarz (1978) Log Criterion0.027459
Schwarz (1978) Criterion1.027839
Craven-Wahba (1979) Generalized Cross Validation0.946132
Hannan-Quinn (1979) Criterion0.978044
Rice (1984) Criterion0.946939
Shibata (1981) Criterion0.943891

\begin{tabular}{lllllllll}
\hline

Multiple Linear Regression - Ad Hoc Selection Test Statistics \tabularnewline

Akaike (1969) Final Prediction Error
0.94537 \tabularnewline Akaike (1973) Log Information Criterion-0.056194 \tabularnewline Akaike (1974) Information Criterion0.945356 \tabularnewline Schwarz (1978) Log Criterion0.027459 \tabularnewline Schwarz (1978) Criterion1.027839 \tabularnewline Craven-Wahba (1979) Generalized Cross Validation0.946132 \tabularnewline Hannan-Quinn (1979) Criterion0.978044 \tabularnewline Rice (1984) Criterion0.946939 \tabularnewline Shibata (1981) Criterion0.943891 \tabularnewline \hline \end{tabular} %Source: https://freestatistics.org/blog/index.php?pk=10070&T=3

[TABLE]

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

[ROW][C]Akaike (1969) Final Prediction Error[/C]
0.94537[/C][/ROW] [ROW][C]Akaike (1973) Log Information Criterion[/C]-0.056194[/C][/ROW] [ROW][C]Akaike (1974) Information Criterion[/C]0.945356[/C][/ROW] [ROW][C]Schwarz (1978) Log Criterion[/C]0.027459[/C][/ROW] [ROW][C]Schwarz (1978) Criterion[/C]1.027839[/C][/ROW] [ROW][C]Craven-Wahba (1979) Generalized Cross Validation[/C]0.946132[/C][/ROW] [ROW][C]Hannan-Quinn (1979) Criterion[/C]0.978044[/C][/ROW] [ROW][C]Rice (1984) Criterion[/C]0.946939[/C][/ROW] [ROW][C]Shibata (1981) Criterion[/C]0.943891[/C][/ROW] [/TABLE] Source: https://freestatistics.org/blog/index.php?pk=10070&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=10070&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 Error0.94537
Akaike (1973) Log Information Criterion-0.056194
Akaike (1974) Information Criterion0.945356
Schwarz (1978) Log Criterion0.027459
Schwarz (1978) Criterion1.027839
Craven-Wahba (1979) Generalized Cross Validation0.946132
Hannan-Quinn (1979) Criterion0.978044
Rice (1984) Criterion0.946939
Shibata (1981) Criterion0.943891






Ramsey RESET test for Misspecification

Multiple Linear Regression - Analysis of Variance
ANOVADFSum of SquaresMean Square
Regression351.97017617.323392
Residual137125.9428680.919291
Total140177.9130451.27080746112
F-TEST18.844296
p-value0

\begin{tabular}{lllllllll}
\hline

Multiple Linear Regression - Analysis of Variance \tabularnewline

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

Regression
351.97017617.323392 \tabularnewline Residual137125.9428680.919291 \tabularnewline Total140177.9130451.27080746112 \tabularnewline F-TEST18.844296 \tabularnewline p-value0 \tabularnewline

Ramsey RESET test for Misspecification

\hline \end{tabular} %Source: https://freestatistics.org/blog/index.php?pk=10070&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]
3[/C]51.970176[/C]17.323392[/C][/ROW] [ROW][C]Residual[/C]137[/C]125.942868[/C]0.919291[/C][/ROW] [ROW][C]Total[/C]140[/C]177.913045[/C]1.27080746112[/C][/ROW] [ROW][C]F-TEST[/C]18.844296[/C][/ROW] [ROW][C]p-value[/C]0[/C][/ROW]

Ramsey RESET test for Misspecification

[/TABLE] Source: https://freestatistics.org/blog/index.php?pk=10070&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=10070&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:

Ramsey RESET test for Misspecification

Multiple Linear Regression - Analysis of Variance
ANOVADFSum of SquaresMean Square
Regression351.97017617.323392
Residual137125.9428680.919291
Total140177.9130451.27080746112
F-TEST18.844296
p-value0






Multiple Linear Regression - Help Regression
VariableParameterS.E.T-STATH0: parameter = 02-tail p-value1-tail p-value
school[t]0.2939260.082773.5511150.000530.000265
workexp[t]0.244580.0573214.2668653.7E-51.9E-5
work2[t]-0.0023740.000618-3.8414480.0001889.4E-5
@F[t]^2-00-3.7254870.0002860.000143
@F[t]^3003.9716930.0001165.8E-5
@F[t]^4-00-4.1406766.1E-53.0E-5
Constant5.3430011.0468665.1038061.0E-61.0E-6

\begin{tabular}{lllllllll}
\hline

Multiple Linear Regression - Help Regression \tabularnewline
VariableParameterS.E.T-STATH0: parameter = 02-tail p-value1-tail p-value \tabularnewline school[t]0.2939260.082773.5511150.000530.000265 \tabularnewline workexp[t]0.244580.0573214.2668653.7E-51.9E-5 \tabularnewline work2[t]-0.0023740.000618-3.8414480.0001889.4E-5 \tabularnewline @F[t]^2-00-3.7254870.0002860.000143 \tabularnewline @F[t]^3003.9716930.0001165.8E-5 \tabularnewline @F[t]^4-00-4.1406766.1E-53.0E-5 \tabularnewline Constant5.3430011.0468665.1038061.0E-61.0E-6 \tabularnewline \tabularnewline \hline \end{tabular} %Source: https://freestatistics.org/blog/index.php?pk=10070&T=5

[TABLE]

[ROW][C]Multiple Linear Regression - Help Regression[/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]school[t][/C]0.293926[/C]0.08277[/C]3.551115[/C]0.00053[/C]0.000265[/C][/ROW] [ROW][C]workexp[t][/C]0.24458[/C]0.057321[/C]4.266865[/C]3.7E-5[/C]1.9E-5[/C][/ROW] [ROW][C]work2[t][/C]-0.002374[/C]0.000618[/C]-3.841448[/C]0.000188[/C]9.4E-5[/C][/ROW] [ROW][C]@F[t]^2[/C]-0[/C]0[/C]-3.725487[/C]0.000286[/C]0.000143[/C][/ROW] [ROW][C]@F[t]^3[/C]0[/C]0[/C]3.971693[/C]0.000116[/C]5.8E-5[/C][/ROW] [ROW][C]@F[t]^4[/C]-0[/C]0[/C]-4.140676[/C]6.1E-5[/C]3.0E-5[/C][/ROW] [ROW][C]Constant[/C]5.343001[/C]1.046866[/C]5.103806[/C]1.0E-6[/C]1.0E-6[/C][/ROW] [ROW][C][/C][/ROW] [/TABLE] Source: https://freestatistics.org/blog/index.php?pk=10070&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=10070&T=5

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 - Help Regression
VariableParameterS.E.T-STATH0: parameter = 02-tail p-value1-tail p-value
school[t]0.2939260.082773.5511150.000530.000265
workexp[t]0.244580.0573214.2668653.7E-51.9E-5
work2[t]-0.0023740.000618-3.8414480.0001889.4E-5
@F[t]^2-00-3.7254870.0002860.000143
@F[t]^3003.9716930.0001165.8E-5
@F[t]^4-00-4.1406766.1E-53.0E-5
Constant5.3430011.0468665.1038061.0E-61.0E-6






Multiple Linear Regression - Help Regression Statistics
Multiple R0.615409
R-squared0.378728
F-TEST13.614419
p-value0
Standard Error0.908223
Observations141
Degrees of Freedom134

\begin{tabular}{lllllllll}
\hline

Multiple Linear Regression - Help Regression Statistics \tabularnewline

Multiple R
0.615409 \tabularnewline R-squared0.378728 \tabularnewline F-TEST13.614419 \tabularnewline p-value0 \tabularnewline Standard Error0.908223 \tabularnewline Observations141 \tabularnewline Degrees of Freedom134 \tabularnewline \hline \end{tabular} %Source: https://freestatistics.org/blog/index.php?pk=10070&T=6

[TABLE]

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

[ROW][C]Multiple R[/C]
0.615409[/C][/ROW] [ROW][C]R-squared[/C]0.378728[/C][/ROW] [ROW][C]F-TEST[/C]13.614419[/C][/ROW] [ROW][C]p-value[/C]0[/C][/ROW] [ROW][C]Standard Error[/C]0.908223[/C][/ROW] [ROW][C]Observations[/C]141[/C][/ROW] [ROW][C]Degrees of Freedom[/C]134[/C][/ROW] [/TABLE] Source: https://freestatistics.org/blog/index.php?pk=10070&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=10070&T=6

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 - Help Regression Statistics
Multiple R0.615409
R-squared0.378728
F-TEST13.614419
p-value0
Standard Error0.908223
Observations141
Degrees of Freedom134






Ramsey RESET Test for Misspecification - Reduction F-test
SSR Ramsey Model110.532401
SSR Reduced Model125.942868
# Reduced Parameters3
Degrees of Freedom134
Reduction F-test6.3669
p-value0.000456

\begin{tabular}{lllllllll}
\hline

Ramsey RESET Test for Misspecification - Reduction F-test \tabularnewline

SSR Ramsey Model
110.532401 \tabularnewline SSR Reduced Model125.942868 \tabularnewline # Reduced Parameters3 \tabularnewline Degrees of Freedom134 \tabularnewline Reduction F-test6.3669 \tabularnewline p-value0.000456 \tabularnewline \hline \end{tabular} %Source: https://freestatistics.org/blog/index.php?pk=10070&T=7

[TABLE]

[ROW][C]Ramsey RESET Test for Misspecification - Reduction F-test[/C][/ROW]

[ROW][C]SSR Ramsey Model[/C]
110.532401[/C][/ROW] [ROW][C]SSR Reduced Model[/C]125.942868[/C][/ROW] [ROW][C]# Reduced Parameters[/C]3[/C][/ROW] [ROW][C]Degrees of Freedom[/C]134[/C][/ROW] [ROW][C]Reduction F-test[/C]6.3669[/C][/ROW] [ROW][C]p-value[/C]0.000456[/C][/ROW] [/TABLE] Source: https://freestatistics.org/blog/index.php?pk=10070&T=7

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=10070&T=7

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


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

Ramsey RESET Test for Misspecification - Reduction F-test
SSR Ramsey Model110.532401
SSR Reduced Model125.942868
# Reduced Parameters3
Degrees of Freedom134
Reduction F-test6.3669
p-value0.000456


Figures (Output of Computation)

Input Parameters & R Code

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