During the start of COVID-19 pandemic it is assumed that rate of spread of *the corona virus is 18% daily. Suppose the spread is assumed to followsimple logistic growth model and ten persons are assumed to haveacquired the virus in province X. Assuming province X with a population of2 million does not follow any necessary protocol to prevent the spread ofthe virus, which of the following models the number of person whoacquired the virus t days after?P(t) =2 000 0001+199 999 e-0.18rOption 1P(t) =2 000 0001 + 199 999 0.18Option 3None of the given choicesP(t) =1+ (2,000 000199 999Option 22 000 000Option 4e-0.18P(t) = 10 0.18

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Answered: During the start of COVID-19 pandemic…

Advanced Engineering Mathematics

10th Edition

ISBN:9780470458365

Author:Erwin Kreyszig

Publisher:Erwin Kreyszig

Chapter2: Second-order Linear Odes

Section: Chapter Questions

Problem 1RQ

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cipate worth S Exercise 2.9.8. [S][Section 2.1][Goal 2.1][Goal 2.5] Counting red blood cells. On page 240 of the April 2011 issue of the Bulletin of the American Mathematical Society Misha Gromov wrote that Erythrocytes are continuously produced in the red bone marrow of large bones... at the rate ~ 2.5 million/second or~ 200 billion/day. Adult humans have 20-30 trillion erythrocytes,~ 5 million/ cubic millimeter of blood. [R54] (a) What is an erythrocyte? (b) Check that 2.5 million/second is about 200 billion/day. (c) Use the numbers in the second sentence to estimate the volume of blood in an adult human, in liters. (d) Check your answer to the previous question with a web search.
11. In this same article on sleep duration and start time, researchers also considered whether school start time was related to obtaining an adequate amount of sleep. An adequate amountbof sleep was considered at least 8.5 hours of sleep, as recommended by the National Sleep Foundation. The authors used logistic regression models to associate the probability of adequate sleep to school start time. Here are some adapted logistic regression results from this study: In(odds of adequate sleep) = 6, + B,, where x1 = school start time, measured as the number of minutes after 7 AM that the school starts. For this model, B,-0.014, SE(B,)-0.005. - What Is the estimated odds ratlo of adequate sleep, and 95% CI, for students who start at 8:30 AM compared to those who start at 7:30 AM? a. 1.01 (1.00, 1.02) b. 1.52 (1.13, 2.05) C. 2.32 (1.27, 4.22) d. 4.05 (1.49, 11.02)
We are interested in estimating the following model log(wage) = Bo + Bieduc + Bzexper + u where • wage=hourly wage, in US dollars; • educ=number of years of education; • exper=number of years of work experience. The variable ctuit is the change in college tuition facing students from age 17 to age 18 and is used as an IV for educ. We run the first stage regression for educ and get the following output: Source s df MS Number of obs 1,230 F (2, 1227) 550.19 Model 3220.84426 2 1610.42213 Prob > F 0.0000 Residual 3591.43541 1,227 2.92700523 0.4728 R-squared Adj R-squared 0.4719 Total 6812.27967 1,229 5.54294522 Root MSE 1.7108 educ Coef. Std. Err. t P>|t| [95% Conf. Interval] ctuit -.1859575 .0608175 -3.06 0.002 -.3052752 -.0666398 exper -.521161 .0157156 -33.16 0.000 -.5519933 -.4903286 _cons 18.63905 .1757961 106.03 0.000 18.29415 18.98394 Is the assumption of instrument relevance satisfied? Why yes, or why not?
My colleagues have conducted an accelerated stability study on a new pharmaceutical protein formulation. They observed a degradation rate of 20% per week at 40°C and a rate of 4% per week at 35°C. What rate can be expected at 25°C?
Consider the following population model for household consumption: cons = a + b1 * inc+ b2 * educ+ b3 * hhsize + u where cons is consumption, inc is income, educ is the education level of household head, hhsize is the size of a household. Suppose a researcher estimates the model and gets the predicted value, cons_hat, and then runs a regression of cons_hat on educ, inc, and hhsize. Which of the following choice is correct and please explain why. A) be certain that R^2 = 1 B) be certain that R^2 = 0 C) be certain that R^2 is less than 1 but greater than 0. D) not be certain
8. In a regression model if you drop one insignificant variable then A. R ^ 2 will decrease but SSE will increase. B. R ^ 2 will increase but SSE will decrease. C. R ^ 2 will increase and SSE also will increase. D. R ^ 2 will decrease and SSE also will decrease. E. None
An ecologist is studying the impacts of air pollution on giant panda populations, specifically on panda birth rates. Due to the historical interest in giant pandas, the ecologist has access to data from the last 30 years that allows her to fit two models to the evolution of giant panda populations over time (one for 30 years and one for the last 5 years after a new chemical plant was built close to the panda habitat). She will use these models to determine the long-term impacts of this pollution on panda populations. She has the following information to help her model the two sets of population dynamics data on adult and baby pandas : i. 1) Without pollution, for every 1 adult female panda a year earlier, 0.75 baby pandas are born each year (reproduction rate = 0.75). 2) With pollution, for every 1 adult female panda a year earlier, 0.7 baby pandas are born each year (reproduction rate = 0.7). 3) 3 in 4 of the adult female pandas survive to the next year. 1 in 3 female baby pandas…
Suppose you obtain the following regression model, E[y]=20+53*x +33*x^2. What is the impact of a 63 unit change of x on the expected value of y when x is at its mean of 54?
The mass of a radioactive substance follows a continuous exponential decay model. A sample of this radioactive substance has an initial mass of 3643 k and decreases continuously at a relative rate of 15% per day. Find the mass of the sample after four days. Do not round any intermediate computations, and round your answer to the nearest tenth. 2445 kg Continue Integrating Evide....docx Type here to search X 5 WRITING-CENTER_....pdf O El PDF revised-summarizi....pdf W Ⓒ2022 McGraw Hill LLC. All Rights Reserved. Terms of Use Subm Privacy Cente 81°F Mostly cloudy. 4
When we fit a logistic regression model with a single explanatory term, x, we often assume that logit (pi) Bo + ₁x₂ where, for the ith observation, p; is the probability of a trial being successful and *; is the value of the explanatory variable. Which of the following is a good reason for using the logit link function? We can't use more than one explanatory term unless we use this link function. It allows us to model nonconstant variance in the response variable. It ensures that the probability of success is between 0 and 1. It ensures that the variance of the response is the same for all observations.
Based on the same logistic regression, what is the probability of having Diabetes among obese people in the simple (i.e.: the proportion of obese people with Diabetes?
Kate-Sasip Question 16 of 20 b. Convert the following log-linear regression model to an exponential model: In(Y) = (0.31) – (0.15x) O Ý = 0.31 · e^(-0.15) x %3D O Y = 0.31 · e ^(0.15) æ Ý = 1.36 · e(-0.15)æ O Y = 1.36 - e(0.15)x O None of the above. SAVE PROGRESS SUBMIT ASSIGNMENT 4:50 PM 4) ENG 2021-02-23 ace 3:

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