Ordinary least squares (OLS) algorithm is widely applied in process measurement

because the sensor model used to estimate unknown parameters can be approximated through multivariate linear model. However

with few or noisy data or multi-collinearity

unbiased OLS leads to large variance. Biased estimators

especially ridge estimator

have been introduced to improve OLS by trading bias for variance. Ridge estimator is feasible as an estimator with smaller variance. At the same confidence level

with additive noise as the normal random variable

the less variance one estimator has

the shorter the two-sided symmetric confidence interval is. However

this finding is limited to the unbiased estimator and few studies analyze and compare the confidence levels between ridge estimator and OLS. This paper derives the matrix of ridge parameters under necessary and sufficient conditions based on which ridge estimator is superior to OLS in terms of mean squares error matrix

rather than mean squares error. Then the confidence levels between ridge estimator and OLS are compared under the condition of OLS fixed symmetric confidence interval

rather than the criteria for evaluating the validity of different unbiased estimators. We conclude that the confidence level of ridge estimator can not be directly compared with that of OLS based on the criteria available for unbiased estimators

which is verified by a simulation and a laboratory scale experiment on a single parameter measurement.