Lagrange Multiplier (LM) test
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 >
Regression Analysis: sqresi versus sqfits
The regression equation is
sqresi = 0.00517 + 0.0196 sqfits
Predictor Coef SE Coef T P VIF
Constant 0.005173 0.001130 4.58 0.000
sqfits 0.019650 0.008395 2.34 0.019 1.000
S = 0.0112996 R-Sq = 0.4% R-Sq(adj) = 0.3%
Analysis of Variance
Source DF SS MS F P
Regression 1 0.0006996 0.0006996 5.48 0.019
Residual Error 1500 0.1915214 0.0001277
Lack of Fit 646 0.0819554 0.0001269 0.99 0.559
Pure Error 854 0.1095659 0.0001283
Total 1501 0.1922209
MTB > let k1 = 1502*0.04
MTB > print k1
Data Display
K1 60.0800
Inverse Cumulative Distribution Function
Chi-Square with 1 DF
P( X <= x ) x
0.95 3.84146
Since nR2 = (1502*0.04) 60.0800 > 3.84146 = , 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.