Polynomial equation with intercept through zero derived from regression model where g sucrose/100 g water is graphed against fpd c. Data was extrapolated from leighton
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A biohybrid material based on poly(lactic acid)(PLA) incorporated with low contents of polyethylene glycol(PEG) functionalized graphene oxide (GO) was prepared via melting process and their structure, morphology, mechanical performance, and thermal properties were studied in detail. SEM and TEM characterizations confirmed that the functionalizationof GO with PEG (PEGmGO) promoted its exfoliationinto thin exfoliated nanosheets, thereby improving the interactions between PEGmGO filler and PLA matrix at interface. FT-IR spectra showed the presence of strong polar and hydrogen bonding interactions between components in the biohybrid. Mechanical and thermal tests indicated that there was the significant improvement of the stiffness, strength, and thermal stability of such biohybrid material with the addition of 0.3 phr PEGmGO, as compared to pure PLA, PEG-plasticized PLA, PEG-plasticized PLA/GO, and other surveyed PEG-plasticized PLA/PEGmGO biohybrids. This behavior was attributed to the homogeneous dispersion of the PEGmGO nanofillerswithin PLA matrix along with their strong interfacial interaction. The as-obtained biohybrids show highly potential to be useful in the bioengineering applications.Simple linear regression estimates exactly how much Y will change when Xchanges by a certain amount. With the correlation coefficient, the variables Xand Y are interchangeable. With regression, we are trying to predict the Yvariable from X using a linear relationship (i.e., a line):
Y = b 0 + b 1 X
We read this as “Y equals b1 times X, plus a constant b0.” The symbol b 0 is known as the intercept (or constant), and the symbol b 1 as the slope for X. Both appear in R output as coefficients, though in general use the term coefficient is often reserved for b 1 . The Y variable is known as the response or dependentvariable since it depends on X. The Xvariable is known as the predictor or independent variable. The machine learning community tends to use other terms, calling Y the target and X a featurevector.
Consider the scatterplot in Figure 4-1displaying the number of years a worker was exposed to cotton dust (Exposure) versus a measure of lung capacity (PEFRor “peak expiratory flow rate”).