The Null Hypothesis - H0: There is no heteroscedasticity i.e. β_{1} = 0
The Alternative Hypothesis - H1: There is heteroscedasticity i.e. β_{1} 0
Reject H0 if nR2 >
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 = , 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.