Impact of ambient temperature set point deviation on Arrhenius estimates

Abstract A least squares model is developed that takes ambient temperature set point error into account when making predictions with the Arrhenius equation. Complete data following a lognormal distribution is assumed. According to the model, the regression can be performed assuming the temperatures are at their desired levels (fixed temperature case). Random variation of the actual ambient temperature about the set point value will inflate the variance or mean squared error but will not bias the estimates. This increases the width of confidence intervals on the parameter estimates and predictions like the MTTF compared to the fixed temperature case. The amount of inflated variance depends chiefly on the extent of set point deviation and activation energy but is also influenced by choice of experimental design.