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Multiple-variable linear regression modeling was used to determine the contributions of molecular antimicrobial resistance determinants to antimicrobial MICs for penicillin, ceftriaxone, erythromycin, clarithromycin, clindamycin, levofloxacin, and trimethoprim-sulfamethoxazole. Training data sets consisting of Canadian\n            <jats:named-content content-type=\"genus-species\">S. pneumoniae<\/jats:named-content>\n            isolates obtained from 1995 to 2019 were used to generate multiple-variable linear regression equations for each antimicrobial.\n          <\/jats:p>","DOI":"10.1128\/aac.01370-21","type":"journal-article","created":{"date-parts":[[2021,10,18]],"date-time":"2021-10-18T21:53:47Z","timestamp":1634594027000},"update-policy":"https:\/\/doi.org\/10.1128\/asmj-crossmark-policy-page","source":"Crossref","is-referenced-by-count":18,"title":["Linear Regression Equations To Predict \u03b2-Lactam, Macrolide, Lincosamide, and Fluoroquinolone MICs from Molecular Antimicrobial Resistance Determinants in\n            <i>Streptococcus 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