Tests for heteroscedasticity, Advanced Statistics

The Null Hypothesis - H0: There is no heteroscedasticity i.e. β1 = 0

The Alternative Hypothesis - H1:  There is heteroscedasticity i.e. β1 0

Reject H0 if nR2 > 1640_Tests for Heteroscedasticity.png

MTB > let c20 = c11*c11 

MTB > let c21 = c15*c15

C20 = sqres

C21 = sqrfits

C11 = RESI1

C15 = FITS1

Regression Analysis: sqres versus sqfits

The regression equation is

sqres = 0.00597 + 0.0168 sqfits

Predictor      Coef   SE Coef     T      P

Constant   0.005967  0.001281  4.66  0.000

sqfits     0.016760  0.009539  1.76  0.079

S = 0.0125463   R-Sq = 0.2%   R-Sq(adj) = 0.1%

Analysis of Variance

Source            DF         SS         MS     F      P

Regression         1  0.0004859  0.0004859  3.09  0.079

Residual Error  1517  0.2387891  0.0001574

Total           1518  0.2392750

MTB > let k1 = 1519*0.02

MTB > print k1

Data Display

K1    30.3800

Inverse Cumulative Distribution Function

Chi-Square with 1 DF

P( X <= x )        x

       0.95  3.84146

MTB > # Since nrsq = 1519*0.02 > chi = 3.8415, we have hetero from LM test

Since nR2 = 30.380 > 3.8415 = 1640_Tests for Heteroscedasticity.png, there is sufficient evidence to reject H0 which suggest that there is heteroscedasticity from the Lagrange Multiplier (LM) test at 5% significance level which means that one or more slopes are not zero.

Posted Date: 3/4/2013 5:44:54 AM | Location : United States







Related Discussions:- Tests for heteroscedasticity, Assignment Help, Ask Question on Tests for heteroscedasticity, Get Answer, Expert's Help, Tests for heteroscedasticity Discussions

Write discussion on Tests for heteroscedasticity
Your posts are moderated
Related Questions
A term commonly encountered in the analysis of the contingency tables. Such type of frequencies are the estimates of the values to be expected under hypothesis of interest. In a tw

Non linear model : A model which is non-linear in the parameters, for instance are   Some such type of models can be converted into the linear models by linearization (the s

Mention the characteristics of Statistics. Explain any two applications of Statistics.

Categorizing continuous variables : A practice which involves the conversion of the continuous variables into the series of the categories, which is common in the field of medical

The risk of being able to recognize the respondent's confidential information in the data set. Number of approaches has been proposed to measure the disclosure risk some of which c


Multiple imputation : The Monte Carlo technique in which missing values in the data set are replaced by m> 1 simulated versions, where m is usually small (say 3-10). Each of simula

a sequence of numbers consist of six 6''s seven 7''s eight 8''s nine 9''s ten 10''s what is the arithmetic mean?

This term sometimes is applied to the model for explaining the differences found between naturally happening groups which are greater than those observed on some previous occasion;

Martingale: In the gambling context the term at first referred to a system for recouping losses by doubling the stake after each loss has occured. The modern mathematical concept