Lecture__2_Class - Data Representation and Analysis...

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Unformatted text preview: Data Representation and Analysis Experimental Design • Step 1: Define experiment and protocol • Step 2: Gather data • Step 3: Standardize data • Step 4: Interpret data Gathering Data Often an art – real data is often “noisy” and often imprecise Glucose measurement in blood – Critical for diabetics Chromatography – Expensive, slow, bulky Biochemical measurements – Fast, miniaturizable Biochemistry of Glucose Sensing Peroxidase as an indicator reaction catalyst [Glucose] mM A 436 nm “Standard Curve” Why use a standard curve? Why does it level off? How do we use the standard curve? A 436 = (slope) x [Glucose] + y-int Curve Fitting • Real data often noisy • Correlation (or curve fit) – Smooths the data – Allows quantitative interpretation of data – Helps to test a hypothesis – Extracts useful parameters out of the data • e.g., μ is useful in bacterial cell growth Linear Curve Fits Clausius-Clapeyron Equation for vapor pressures: or 1/T lnP sat What are A and B?...
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Lecture__2_Class - Data Representation and Analysis...

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