The article "Copper Oxide Mounted on Activated Carbon as Catalyst for Wet Air Oxidationof Aqueous Phenol1. Kinetic and Mechanistic Approaches" (P. Álvarez, D. McLurgh, andP. Plucinski, Industrial Engineering and Chemistry Research, 2002: 2147-2152) reports theresults of experiments to describe the mechanism of the catalytic wet air oxidation ofaqueous phenol. In one set of experiments, the initial oxidation rate (in kilograms of phenolper kilogram of catalyst per hour) and the oxygen concentratioan (in mol/m) were measured.The results (read from a graph) are presented in the following table.Rate (y)0.44 0.49 0.60 0.640.723.84 4.76 6.08 7.06 8.28O, concentration (x)a Itis known that x and y are related by an equation of the form y = kx, where r is theoxygen reaction order. Make appropriate transformations to express this as a linearequation.b. Estimate the values of k and r by computing the least-squares line.Based on these data, is it plausible that the oxygen reaction order is equal to 0.5?Explain.

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MATLAB: An Introduction with Applications

6th Edition

ISBN:9781119256830

Author:Amos Gilat

Publisher:Amos Gilat

Chapter1: Starting With Matlab

Section: Chapter Questions

Problem 1P

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0/2740c2d723d0000e7/root/M1_E06_Communication_Question%20(2).pdf 1: MRE- S US HISTCRY /1I: MR. S PHCTO STUDIO: M. S INTEGRATED MATH. S CHEMISTRY: MRH- 100% + The bacterium E. coli often causes illness among people who eat infected food. Suppose that five E. coli bacteria in a batch of ground beef begins doubling every 10 minutes. Show all work leading to your answers. a. Complete the table below that represents the number of bacteria after x minutes. (Assume no bacteria die.) Number of minutes 10 20 Number of time periods, x Number of bacteria, y b. Explain how to write an equation that will allow us to find the number of bacteria after any number of minutes. C. Write the equation below: d. How many bacteria will there be after two hours? Show your work Sign out IN
Consider the following panel model to examine the effect of retirement on consumption expenditure, consit, of individual i over years t=1,…,3: (B1) log⁡(consit) = β0 + β1retiredit + β2ageit + β3marriedit + β4healthit + δ1Yr2t + δ2Yr3t + ai + uit Where: retiredit is a dummy variable equal to 1 if individual i is retired on year t and 0 otherwise ageit is the individual's age in years marriedit is an indicator variable for whether the individual is married (1) or not (0) in year t healthit is an indicator variable equal to 1 if the individual is in 'good health' and 0 otherwise Yr2 is a dummy variable equal to 1 in year t=2 and 0 otherwise Yr3 is a dummy variable equal to 1 in year t=3 and 0 otherwise Using the information above, answer the following 3 questions. [i] Give two (2) examples of the kind of variables captured by the term ai in Model (B1). [ii] What is the crucial assumption we must make so that the random effects (RE) estimator is consistent? Under this assumption, why is…
Look at the following regression table where the dependent variable is the demand for illegal massage services in a city in the United States. Specifically, the dependent variable is the number of customers per hour (Models 1 and 2) or per day (Models 3 and 4). (a) Explain why the coefficient for Population/1,000 in Model 2 is very different from the one in Model 4?(b) Can you reject H0 in Model 1 if H0 : βP opulation/1,000 = 0.01, H1 : βPopulation/1,000 6= 0.01, and α = 0.01?
I want to assess whether or not students produce more stress hormones (cortisol) after watching different types of movies. I collect saliva samples (to assess cortisol, stress) from students after they watch an action movie and then another (two days later) after they watch a horror movie. The amount of stress hormone in the saliva is measured as ng/mL.Test the hypothesis that saliva cortisol levels will be different between the two genres of movies at an alpha of 0.05. (Have action - horror be set for your differences). Data: Action Movie Horror Movie 10 12 -2 15 18 -3 16 17 -1 20 21 -1 24 22 2 24 23 1 26 27 1 13 16 -3 18 20 -2 19 23 -4 20 15 5 24 25 1 What is the obtained t value? What conclusion do you draw?
A) Compute the corresponding effect size(s) and indicate magnitude(s). d= / Magnitude = r2 = / Magnitude = B) Compute the appropriate test statistic(s) to make a decision about H0. critical value = test statistic= d= / Magnitude = r2 = / Magnitude = C) Compute the appropriate test statistic(s) to make a decision about H0 critical value = test statistic= d= / Magnitude = r2 = / Magnitude =
Consider the following simple linear regression model, Y; = Po + B₁X₁ + εi, for i=1,2,...,n, where &'s are all independent and normally distributed with E(₁) = 0, and Var(₁) = 0². i) Check whether a statistic Y = Y + B₁ (X₁-X) is an unbiased estimator of the mean of the response variable E(Y) or not. Justify your conclusion.
A number of studies have shown lichens (certain plants composed of an alga and a fungus) to be excellent bio-indicators of air pollution. An article gives the following data (n = 13) on x = NO3- wet deposition (g N/m²) and y = lichen (% dry weight): x 0.05 0.10 0.11 0.12 0.31 0.37 0.42 0.58 0.68 0.68 0.73 0.85 0.92 y 0.53 0.58 0.45 0.54 0.57 0.53 1.03 0.89 0.90 1.02 0.91 1.00 1.66 Predictor Constant no3 depo Estimate 0.39329 0.9286 0.1767 S = 0.1866 R-sq = 71.5% Standard Error 0.09566 t Stat 4.11 5.26 p-value 0.002 0.000 R-sq (adj) = 68.9% (a) What are the least square estimates of B and ẞ₁? (Round your answers to three decimal places.) bo = = (b) What is the margin of error for a 95% CI for ẞ₁? (Round your answer to four decimal places.) Margin of Error = (c) Predict lichen N for an NO3 - deposition value of 0.7. (Round your answer to three decimal places.) % dry weight (d) What is the estimate of σ? (Round your answer to four decimal places.) S = % dry weight

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