Parks test, Advanced Statistics

Assignment Help:

The Null Hypothesis - H0: β1 = 0 i.e. there is homoscedasticity errors and no heteroscedasticity exists

The Alternative Hypothesis - H1: β1 ≠ 0 i.e. there is no homoscedasticity error and there is heteroscedasticity

MTB > let c33=loge(c20)

MTB > let c34=loge(c7)

MTB > let c35=loge(c8)

MTB > let c36=loge(c9)

MTB > let c37=loge(c10)

C33 = lnsqres

C34 = lntotexp

C35 = lnincome

C36 = lnage

C37 = lnnk

 

Regression Analysis: lnsqres versus lntotexp

The regression equation is

lnsqres = - 5.41 - 0.155 lntotexp

 

Predictor     Coef  SE Coef      T      P

Constant   -5.4069   0.6430  -8.41  0.000

lntotexp   -0.1550   0.1420  -1.09  0.275

 

S = 2.15075   R-Sq = 0.1%   R-Sq(adj) = 0.0%

Analysis of Variance

Source               DF        SS     MS     F      P

Regression         1     5.515  5.515  1.19  0.275

Residual Error  1517  7017.227  4.626

Total                1518  7022.743

Since β1 ≠ 0 and is 0.155, H1 would be accepted suggesting that there are no homoscedasticity errors but there is indication that there is heteroscedasticity.

 

Regression Analysis: lnsqres versus lnincome

The regression equation is

lnsqres = - 5.77 - 0.070 lnincome

 

Predictor     Coef  SE Coef      T      P

Constant   -5.7687   0.7111  -8.11  0.000

lnincome   -0.0698   0.1465  -0.48  0.634

 

S = 2.15143   R-Sq = 0.0%   R-Sq(adj) = 0.0%

Analysis of Variance

Source               DF        SS     MS     F      P

Regression         1     1.050  1.050  0.23  0.634

Residual Error  1517  7021.693  4.629

Total                1518  7022.743

Since β1 ≠ 0 and is 0.070, H1 would be accepted suggesting that there are no homoscedasticity errors but there is indication that there is heteroscedasticity.

Regression Analysis: lnsqres versus lnage

The regression equation is

lnsqres = - 7.23 + 0.315 lnage

 

Predictor     Coef  SE Coef      T      P

Constant   -7.2276   0.9125  -7.92  0.000

lnage         0.3155   0.2563   1.23  0.219

 

S = 2.15052   R-Sq = 0.1%   R-Sq(adj) = 0.0%

 

Analysis of Variance

Source                DF        SS     MS     F      P

Regression          1      7.007  7.007  1.52  0.219

Residual Error    1517  7015.736  4.625

Total                  1518  7022.743

Since β1 ≠ 0 and is 0.315, H1 would be accepted suggesting that there are no homoscedasticity errors but there is indication that there is heteroscedasticity.

Regression Analysis: lnsqres versus lnnk

The regression equation is

lnsqres = - 5.99 - 0.281 lnnk

Predictor     Coef        SE Coef           T      P

Constant   -5.98771  0.08819  -67.89  0.000

lnnk           -0.2812   0.1631   -1.72  0.085

 

S = 2.14949   R-Sq = 0.2%   R-Sq(adj) = 0.1%

Analysis of Variance

Source            DF        SS          MS            F      P

Regression      1       13.738    13.738  2.97  0.085

Residual Error 1517  7009.004  4.620

Total               1518  7022.743

Since β1 ≠ 0 and is 0.281, H1 would be accepted suggesting that there are no homoscedasticity errors but there is indication that there is heteroscedasticity.

MTB > # lntotexp is significant and estimate of beta/2 is -0.155/2 or -0.775


Related Discussions:- Parks test

Bartlett''s test for variances, Bartlett's test for variances : A test for ...

Bartlett's test for variances : A test for equality of the variances of the number (k)of the populations. The test statistic can be given as follows   where s square is an

Ecm algorithm, This is extension of the EM algorithm which typically conver...

This is extension of the EM algorithm which typically converges more slowly than EM in terms of the iterations but can be much faster in the whole computer time. The general idea o

Historigram, difference between histogram and historigram

difference between histogram and historigram

Greenhouse geissercorrection, Greenhouse geissercorrection is the method o...

Greenhouse geissercorrection is the method of adjusting the degrees of freedom of the within- subject F-tests in the analysis of the variance of longitudinal data so as to allow t

Collapsing categories, Collapsing categories : A procedure generally applie...

Collapsing categories : A procedure generally applied to contingency tables in which the two or more row or column categories are combined, in number of cases so as to yield the re

January 2015 Take-Home Assignment, 3. a. A researcher in Hong Kong computes...

3. a. A researcher in Hong Kong computes the correlation between the percentage of employee turnover and the local unemployment rate (also expressed as a percentage) over a 20-mont

Independent component analysis (ica), Independent component analysis (ICA) ...

Independent component analysis (ICA) is the technique for analyzing the complex measured quantities thought to be mixtures of other more fundamental quantities, into their fundamen

Intercropping experiments, Intercropping experiments are the experiments i...

Intercropping experiments are the experiments including growing two or more crops at same time on the same patch of land. The crops are not required to be planted nor harvested at

Financial Econometrics Assignment help- postgarduate, Hi , Im currently ta...

Hi , Im currently taking the course Financial Econometrics of Master of Finance at RMIT. I find it really difficult to understand the course''s material and now im having the majo

Quantile regression, Quantile regression is an extension of the classical ...

Quantile regression is an extension of the classical least squares from estimation of the conditional mean models to the estimation of the variety of models for many conditional q

Write Your Message!

Captcha
Free Assignment Quote

Assured A++ Grade

Get guaranteed satisfaction & time on delivery in every assignment order you paid with us! We ensure premium quality solution document along with free turntin report!

All rights reserved! Copyrights ©2019-2020 ExpertsMind IT Educational Pvt Ltd