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The importance of liquid viscosity in chemical process design makes it one of the most measured transport properties. Nevertheless, in the pure-component database, no experimental data on liquid viscosity for nearly 50% of the compounds are available. Therefore, prediction methods for liquid viscosity of alkenes over a wide range of absolute temperature for each components are necessary. Moreover, experimental data measured at lower temperatures are often extrapolated to higher temperatures with erroneous results. To improve liquid viscosity prediction of experimental data to temperatures and carbon numbers, we propose an empirical rule for estimating the viscosity of alkenes compounds. A predictive method, based physical properties (absolute temperature and carbon numbers) as its inputs, to correlate liquid viscosity by the statistical analysis is proposed. For a group of 19 compounds, the mean average absolute deviation was 4.6% for 118 data points. These values are better than other predictive methods and show that the statistical analysis model is stable and can be used to obtain good predictions for compounds that were not used in the model calibration. [ABSTRACT FROM AUTHOR]