INTRODUCTION
The seriousness of the road accident problem warrants a deeper investigation into the possible causes. In particular the ULFR model is investigated. The studies of functional and structural relationships have been focused on estimating the parameters and the measurement error models since it was first introduced by Adcock in year 1877 (Sprent, 1990). Several methods were proposed, but the widely used estimator is the maximum likelihood method (Dolby, 1976; Fuller, 1987; Kendall and Stuart, 1979). An early study to check the adequacy of the ULFR model is given by Fuller (1987) who suggests using the residual plot as an indication of nonlinearity, lack of homogeneity of the error variances, nonnormality of the errors and outlier observations. In this study we propose the COD as a measure to check the model adequacy and the assumption of independent variable. Without loss of generality, the properties of this measurement for case λ = 1 is investigated. A large value of COD of the ULFR relative to the COD of ordinary Simple Linear (SL) regression model is indicative of causation.
As pointed out by Fuller (1987), the assumption that the independent variable
can be measured exactly may not be realistic in many situations. The estimates
of independent variable may contain measurement error arising from trying to
quantify a variable that has no physical dimension. For example in the road
accident problem, it has been shown that the male motorist is more prone to
be involved in accident. One possible explanation is the performance of male
drivers may vary with state of mind. Since this factor, the state of mind cannot
be measured directly, it is more reasonable to consider them as a measurement
error rather than introduce a new independent variable into the model.
UNREPLICATED LINEAR FUNCTIONAL RELATIONSHIP MODEL
Suppose that X and Y are two linearly related unobservable variables
and the two corresponding random variables x and y are observed with error δ and ε, respectively as
The following conditions are assumed
where, δ_{i} and ε_{i} are mutually independent and
normally distributed random variables. Equation 1 and 2
are known as ULFR model when there is only one relationship between the two
variables X and Y. When the ratio of the error variances is known, that is ,
then the maximum likelihood estimators of parameters α, β, and
X_{i} are:
and
where,
DERIVATION OF THE COEFFICIENT OF DETERMINATION (COD)
In an ordinary SL regression analysis, we look at the COD as a measure of the variability in y explained by the regression model. The construction of the COD is a good practice when fitting the ULFR model.
The Eq. 1 and 2 can be rewritten as
If we substitute X_{i} by (x_{i}δ_{i}) in (8), then we have the expression
Where, the errors of the model , V_{i} = (ε_{i}  βδ_{i})
= y_{i}  (α + βx_{i}) for = 1, 2, …, n is a
normally distributed random variable with zero mean and variance .
If are the estimates of α and β respectively, then
will be the residual of the model. From Kendall and Stuart (1979) and Andrson (1984), that the sum of squared distances of the observed points from the fitted line or the residual sum of squares (SS_{E}) is given as:
We shall consider here that the ratio of the error variances is equal to one
(λ = 1). For those cases when λ ≠ 1, we can always reduce this
to the case of λ = 1 by dividing the observed values of y by λ^{1/2} (Kendall
and Stuart, 1979). Hence, we have:
In the same way as ordinary linear regression, we can now define the COD of
the ULFR ()
as the proportion of variation explained by the variable x, that is:
where, SS_{R} is the regression sum of squares which can be derived as:
We can summarise our proposed COD with the following results.
Result 1: Let the ratio of the error variances be known and is equal
to one,
= λ = 1, then the COD for the ULFR model is
Proof: When λ = 1, then
in Eq. 5 becomes
and
Notice that
From Eq. 13,
we have,
From the R.H.S.
Result 2: Let
and
be the slope estimator for the simple linear regression and the ULFR, respectively.
The corresponding COD are:
Then .
Proof: From the regression sum of squares, we obtain
we also know that simple linear regression has .
We now proof
by contradiction. Assuming that
Also from ,
this is a contradiction. Hence, we have ..
PROPERTIES OF
Since
is computed following the same method as ,
some of the properties of
may also remain for .
It is known that
does not measure the appropriateness of the linear model (Montgomery and Peck,
1992). This holds for
too. As an example, when a nonlinear model has a large
value, it is obvious that
will also be large (Result 2).
Secondly, the magnitude of
depends on the range of variability in the variables X and Y. From Eq.
14, we have
Let var(X) and var(Y) be variances of X and Y, respectively then and var(Y)
= S_{yy}/ n1. We consider three possible cases of relationship between
var(X) and var(Y) when S_{xx }> 0 and S_{yy} > 0.
Case 1: If var(X) = var(Y) ⇔ S_{xx} = S_{yy}, then
when compared with Eq. 14, this holds when
= 1.
Case 2: If var(X) < var(Y) ⇔ S_{yy}/k = S_{xx} < S_{yy} for k > 1, then
when compared with Eq. 14, this holds when
>1.
Case 3: If var(X) > var(Y) ⇔ kS_{yy} = S_{yy} for k>1, then
when compared with Eq. 14, this holds when <1.
We can summarize the above properties with the following result.
Result 3: Let S_{xx} > 0, S_{yy} > 0 and Sxx = kS_{yy} or var(x) = k var(y), then
The numerical example of the above results is given in Fig. 1a1f.
To explain the relationship between
and ,
the value of CODs are compared for different sample sizes (n = 10, 50, 100,
1000, 5000 and 10000). For a given sample size, fifty samples were generated
from a uniform distribution, each contains two random data sets (y_{i},
x_{i}). As an example, Fig. 1a shows fifty values
of COD derived from SL and ULFR methods with sample size n = 10. Figure
1b1f show the same plot with sample sizes n = 50, 100,
1000, 5000 and 10000, respectively. It is clearly shown that
is always greater or equal to .
One interesting feature shown in Fig. 1 is that both
and
decreased when the sample size increased.
APPLICATION IN ROAD ACCIDENT PROBLEM
So far, various road accident models have been proposed to explain Malaysian road accident phenomena (Radin et al., 1995; Radin, 1996; Razali and Dahalan, 1992). However, PDRM road statistics show that the number of road accidents still remains high. One reason the Malaysian road accident problem has not been solved effectively is that the existing models are constructed for prediction purpose rather than causal inference. The crucial factor ignored in the existing models is the driver ’s state of mind that cannot be measured directly. The driver state of mind includes driver ’s attitude, ability to concentrate and tendency to take risks. It has been suggested that the driver ’s state of mind is an essential factor that causes road accident (Leeming, 1969; Forbes, 1972) .
In this example, we introduce the state of mind as a factor that causes variations
in other physically measurable factors such as driver ’s sex, age and race.
This is formally expressed using the ULFR model where the measurement error,
δ is representing the state of mind. Difference between
and
is used to measure the effect of the state of mind to the road accident model.
Since
and
have similar interpretations by construction, when
is significantly larger than
we say that the state of mind has been incorporated into the independent variable
and the model now explains the causal relationship of road accident.
Table 1 shows the sociobiological variables of the Malaysian
road accident data for 1996 obtained form Malaysian Royal Police (PDRM) headquarters.
The BoxCox power transformation (Box and Cox, 1964) is presented to access
normality with
= [0.25 0.69 1.25 0.14 0.23 0.47]. Without loss of generality, we assume that
the ratio of variances, λ, equals one by dividing the number of state accidents
by the total number of accidents in Malaysia.

Fig. 1a: 
COD plots for SL and ULFR models with n = 10 

Fig. 1b: 
COD plot for SL and ULFR models with with n = 50 

Fig. 1c: 
COD plots for SL and ULFR models with n
= 100 

Fig. 1d: 
COD plot for SL and ULFR models with n
= 1000 

Fig. 1e: 
COD plots for SL and ULFR models with n
= 5000 

Fig. 1f: 
COD plot for SL and ULFR models with with
n = 10000 
Table 1: 
Sociobiological explanatory variables 

Source: PDRM (Malaysian Royal Police, 19951998)
Note:
• 
The number of vehicles involved in 1996 does not include accident
of type ‘slightly injured ’ and property damaged only 
• 
Y = number of road accidents ( ‘000)
P_{1} = Age (ratio of drivers aged 1630 years to total number of
drivers involved in accidents)
P_{2} = Race (ratio of Chinese drivers to total number of drivers
involved in accidents)
P_{3} = Number of serious and fatal accidents due to influence of
alcohol ( ’00)
P_{4} = Experience (ratio of vehicles with drivers of less than
5 years ’ experience to total number of vehicles involved in accidents)
P_{5} = Sex (ratio of male drivers to total number of drivers involved
in accidents) 
• 
The numbers given are ratios explained in (2). For example,
0.28 in column two row six means that for every 100 cases of road accident
in Selangor during 1996 about 28 cases involved Chinese drivers. 
First, we look at the estimation of parameters for the ULFR model containing the state of mind measurement error for drivers age (P_{1}) is given as
therefore, the ULFR model is
Table 2: 
Estimated parameters, coefficient of determination for the
ULFR and SL models and the effect of measurement error 

with the coefficient of determination,
= 0.4408
(see Result 3). Comparing with the SL model
the result shows that the ULFR model explains the variability of Y better than
the SL model (
>R^{2}). The SL model explains 12.31% of the variance of the number
of road accidents but it increases to 44.08% when conditioned to ‘state
of mind ’ factor. This suggests that the state of mind explains 31.77%
(E_{e} = )
of the variance of road accident number.
It is clearly show (Table 2) that the ULFR model gives a better fit than the SL model with higher COD. The SL model indicates that variables P_{1}, P_{3} and P_{5} are not significantly contribute to the road accident rates. The first interpretation opposes other studies (Arthur and Little, 1970; IATSS, 1988; OECD Road Research Group, 1978; OECD Scientific Export Group, 1986) that age, gentle and alcoholic are important factors. However, when the state of mind is introduced, the three variables, especially P_{5} become important and it may use to explain causal relationship of the road accident. For example, we say the younger driver may be the cause of higher accident rates because of their driving attitude or tendency to take risks.
The introduction of state of mind does not increase the COD of variables P_{2}
and P_{4} significantly. This suggests that the driver ’s race
and experience are two exact predictor of road accident. On the other hand,
both
and
indicate race and driving experience are not significantly explained the road
accident model. Henceforth, only variables P_{1}, P_{3} and
P_{5} are recommended for further analysis such as multiple regression.
CONCLUSION AND FURTHER WORK
In this study, the COD of ULFR is proposed and it properties are investigated.
The proposed COD and the COD of ordinary linear regression model are used to
investigate the effect of measurement error and hence try to explain the causal
relationship of the model. It is a useful way to check whether a variable is
reasonably assumed to be independent. This study on the Malaysian road accident
problem demonstrates that the effect of the state of mind, indicated by the
measurement error is clear but different in all factors.
However, confirmation of the causal properties in the ULFR model may be further
strengthening by studying further the distribution of .
The multivariate version of ULFR may also be considered in further studies.