MULTIPLE LINEAR REGRESSION (MLR) MODELS FOR PREDICTING CHRONIC ALUMINUM TOXICITY TO FRESHWATER AQUATIC ORGANISMS AND DEVELOPING WATER QUALITY GUIDELINES.

scientific article

MULTIPLE LINEAR REGRESSION (MLR) MODELS FOR PREDICTING CHRONIC ALUMINUM TOXICITY TO FRESHWATER AQUATIC ORGANISMS AND DEVELOPING WATER QUALITY GUIDELINES. is …
instance of (P31):
scholarly articleQ13442814

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P356DOI10.1002/ETC.3922
P698PubMed publication ID28833517

P50authorKevin V BrixQ88362844
William AdamsQ88893583
David K DeForestQ89745898
P2093author name stringLucinda M Tear
P2860cites workResponse of fish and macroinvertebrate bioassessment indices to water chemistry in a mined Appalachian watershedQ33280088
CHRONIC TOXICITY OF ALUMINUM, AT A PH OF 6, TO FRESHWATER ORGANISMS: EMPIRICAL DATA FOR THE DEVELOPMENT OF INTERNATIONAL REGULATORY STANDARDS/CRITERIA.Q38698969
Use of Multiple Linear Regression Models for Setting Water Quality Criteria for Copper: A Complementary Approach to the Biotic Ligand ModelQ38836945
Development and application of a biotic ligand model for predicting the chronic toxicity of dissolved and precipitated aluminum to aquatic organisms.Q47697690
Evaluating the effects of pH, hardness, and dissolved organic carbon on the toxicity of aluminum to freshwater aquatic organisms under circumneutral conditionsQ47917766
Multimodel Inference: Understanding AIC and BIC in Model SelectionQ56390541
A protocol for data exploration to avoid common statistical problemsQ60015513
P433issue1
P921main subjectaluminiumQ663
fresh waterQ102192
water qualityQ625376
linear regressionQ10861030
P304page(s)80-90
P577publication date2017-08-22
P1433published inEnvironmental Toxicology and ChemistryQ3726418
P1476titleMULTIPLE LINEAR REGRESSION (MLR) MODELS FOR PREDICTING CHRONIC ALUMINUM TOXICITY TO FRESHWATER AQUATIC ORGANISMS AND DEVELOPING WATER QUALITY GUIDELINES.
P478volume37