Multiple linear regression was used to quantify the dependence of the antimicrobial activity of 13 peptides upon three calculated or experimentally determined parameters: mean hydrophobicity, mean hydrophobic moment, and alpha-helix content. Mean hydrophobic moment is a measure of the amphiphilicity of peptides in an alpha-helical conformation. Antimicrobial activity was quantified as the reciprocal of the measured minimal inhibitory concentration (MIC) against Escherichia coli. One of the peptides was magainin 2, and the remainder were novel peptides designed for this study. The multiple linear regression results revealed that the amphiphilicity of the peptides was the most important factor governing antimicrobial activity compared to mean hydrophobicity or alpha-helix content. A better regression of the data was obtained using ln(1/MIC+constant) as the dependent variable than with either 1/MIC or ln(1/MIC). These results should be useful in designing peptides with higher antimicrobial activity.