matlab question only tell me the steps that I need to do for question a. 
Background Estrogens are a group of compounds named for their importance in both menstrual and estrous reproductive cycles (Wikipedia). Estrogen or similar compounds can be found in a large number of consumer products (cosmetics, pharmaceuticals, etc.). Various health and environmental concerns are associated with estrogenic compounds being released into the environment (eg. feminization of fish). Estrogens and other endocrine-disrupting compounds (EDCs) can be potent at very low concentration and can be hard to detect chemically. Symergistic effects of multiple compounds make their detection and risk assessment more challenging Instead of chemical analysis the yeast estrogen screen (YES)-assay is oten used. The assay uses a recombinant yeast strain that carries a human estrogen receptor. If the estrogen receptor detects estrogen, it catalyzes a colorimetric reaction. This colour change can be quantified in simple lab experiments. Typically, dilution series of samples are prepared using two-fold dilutions. The response at each dilution is measured to generate a dose-response profile, as showrn in Figure 1 2.8 2.6 2.4 2.2 1.8 1.6 1.4 1.2 0.8 10 10 10 Estrogen concentration in mg/L 10 10 Figure 1: Dose-response curve of YES-assay. The circles are measured data-points and the solid line represent a best fit using the Hill equation. Data from the YES assay is typically evaluated by fitting the data to a mathematical model. The Hill equation is a simple way to do so: (b-a) OD-a+ Where OD is the optical density at 540nm (measured response data) and c is the concentration of the estrogenic compound (mg/L). The model coefficients a, b, m and ECso represent: a: the background OD (0.9 in Figure 1), b: the saturation OD (-2.7 in Figure 1), c the slope, and ECs: the concentration at which half of the maximum response is achieved 1.5 x 103 mg/L in Figure 1). Typically, non-linear regression analysis is undertaken to obtain estimates for ECso which can then be used as a quantification of estorenicity. The regression analysis requires experimental data (e.g. the circles in Figure 1). Assignment: A lab technician conducted the YES assay for 8 samples, but is struggling with the data analysis. You were given a data-file (data.mat) that contains 6 arrays Two arrays contain the experimental data: E2 a 1x12 vector containing the samples' concentrations (c in equ. (1), the same for all 8 samples) a 8x12 vector containing the response of the YES assay (one sample per row for a total of 8 samples) OD The other four contain the fitted parameters: EC50 a 8x1 vector containing the estimated coefficient ECso for each sample a a 8x1 vector containing the estimated coefficient a for each sample b a 8x1 vector containing the estimated coefficient b for each sample m a 8x1 vector containing the estimated coefficient m for each sample Visualize the data: Create a 4 by 2 sub-plot and plot the response (OD) for each sample in one of the sub-plots (always plot against the concentration stored in E2 and use open circles as the symbol for the data). Please note that the concentrations (x-axis) that were used to generate the data resulted of two-fold dilutions. It is therefore useful to plot with a logarithmicx-axis (see Figure 1). A useful Matlab command for this purpose is semilogx (can be used the same way as plot) a)
