Laserfiche WebLink
Colorado State University (Department of Environmental Health, Fort Collins, Colorado). All <br />tissue concentrations aze based on dry-weight determinations. <br />Statistical analysis <br />The data generated by the dietary study and acute tests represent concentration-response <br />1 <br /> <br /> <br /> <br />1 <br /> <br /> <br /> <br /> <br /> <br /> <br />relationships. For both data sets, it was of interest to determine if the observed concentration <br />responses were influenced by the experimental treatments (i.e., dietary exposure or fish species). <br />Consequently, regression analysis was used to estimate statistical models that describe each data <br />set, and to determine if fitted lines had similar slopes and intercepts (Oris and Bailer 1997). <br />Survival data were analyzed using logistic regression. PROC GENMOD (with options <br />LINK=LOGIT, DIST=BINOMIAL, DSCALE; SAS 1993) was used to describe the response of <br />survival as a function of the independent variables. The full regression model had the form <br />where p =survival proportion, logit (p) =natural log [ p / (1 - p)], ~o =intercept, <br />logit(p)=~o+~~X~+RzX2+R~ZX~Xz <br />(3l, Rz =coefficients of linear terms, X, =dissolved selenium concentration, XZ = dietazy selenium <br />concentration or fish species, and (3,2 =coefficient of cross products. The coefficient of cross <br />products tests for equal slopes of the two regression lines. A significant coefficient suggests that <br />the regression lines are not parallel and further analyses are not required to demonstrate that the <br />concentration-response relationships are different from each other. A nonsignificant coefficient <br />suggests that the lines are pazallel and may or may not have similar intercepts. In this case, <br />additional analyses are required. Since the coefficient of cross products is not significant, it is <br />omitted from the statistical model and the analysis is re-run. Interpretation of the reduced model <br />8 <br />