iurve fitting using a custom equation and noisy data. Experimental data will inevitably have some degree of noise. a. Create a bacterial population with a degree of random noise using the command A=Pexp(kt)+500randn(1,length(t)); P,k, and t are defined as per problem 6. randn(1,length (t)) generates a vector of random numbers from a Gaussian distribution with a mean of 0 and a standard deviation of 1 . We add these random numbers to our bacterial population to simulate noise in the measurement. b. In this problem we will fit the equation A(t)=Bt+Pekt The 'fittype' command creates a fittype object to be used with 'fit'. We can specify any equation here. However, the independent variable must be x. fitEq = fittype (Bx+Pexp(kx)) The variable fitEq is used in place of 'exp1' in the fit command shown above in problem 6. c. Fit the equation to the data and display the curve fit object and the goodness of fit. d. Plot the data (blue points) and the fit (red line) against time. Add a comment to your code discussing if the best-fit value for ' B ' is actually different than zero (consider the 95% confidence intervals in 'gof')