Q 1. The RMU community of 3,000 people is homogeneously mixed. Assume that one person (staff or student) returned to the community with COVID-19. There exists a mathematical model, which assumes that diseases tend to spread at a rate in direct. proportion to the mmber P who have the disease and to the mumber 3, 000- P who have not yet contracted the disease (logistic growth). The mathematical model i, 3000 P(t) 1+ 2999e 12 where P(t) is the number of people who have contracted the discase after t days. (a) How many people would have contracted the discase after 5, 10, 15, 20, 25 and 30 days? (b) How many days will it take until half of the community to have contracted COVID-19? th CamScanņer ) How many days will it take until the entire commmity contracts COVID-19?

Q 1. The RMU community of 3,000 people is homogeneously mixed. Assume that one person (staff or student) returned to the community with COVID-19. There exists a mathematical model, which assumes that diseases tend to spread at a rate in direct. proportion to the mmber P who have the disease and to the mumber 3, 000- P who have not yet contracted the disease (logistic growth). The mathematical model i, 3000 P(t) 1+ 2999e 12 where P(t) is the number of people who have contracted the discase after t days. (a) How many people would have contracted the discase after 5, 10, 15, 20, 25 and 30 days? (b) How many days will it take until half of the community to have contracted COVID-19? th CamScanņer ) How many days will it take until the entire commmity contracts COVID-19?

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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Table 4.18 shows estimated effects for a fitted logistic regression model with squamous cell esophageal cancer (1 = yes, 0 = no) as the response variable Y. Smoking status (S) equals 1 for at least one pack per day and 0 other- wise, alcohol consumption (A) equals the average number of alcoholic drinks consumed per day, and race (R) equals 1 for blacks and 0 for whites. A. To describe the race-by-smoking interaction, construct the prediction equation when R = 1 and again when R = 0. Find the fitted YS conditional odds ratio for each case. Similarly, construct the prediction equation when S = 1 and again when S = 0. Find the fitted YR conditional odds ratio for each case. Note that, for each association, the coefficient of the cross-product term is the difference between the log odds ratios at the two fixed levels for the other variable. B. In Table 4.18, explain what the coefficients of R and S represent, for the coding as given above. What hypotheses do the P -values refer to for…
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Question 7777
From 2000 through 2009, the population of State A grew more slowly than that of State B. Models that represent the populations of the two states are given by P = 37.5t + 6075 State A P = 168.2t + 5138 State B where P is the population (in thousands) and t represents the year, with t = 0 corresponding to 2000. Use the models to estimate when the population of State B first exceeded the population of State A. (Round your answer to the nearest year.)
6. From 1790 to 1860, U.S. population grew rapidly (see the table). Let f(t) be the population t years after 1790. a) Create a scattergram for the data on your Graphing Calculator. b) Based on your scattergram, what appears more appropriate, a linear model, or an exponential model? Explain Year 1790 1800 1810 1820 1830 1840 1850 1860 d) What is the base? What does it tell us about the rate of population growth? Population (millions) 3.9 5.3 7.2 9.6 12.9 17.1 23.2 31.4 c) Use ExpReg on your GC to find an equation to model the situation. Write your answer using function notation. Round using 6 decimal places.
In terms of the model parameters, state the null hypothesis that, after controlling for sales and roe, ros has no effect on CEO salary. State the alternative that better stock market performance increases a CEO’s salary.
Consider the following estimated model, where the dependent variable is the log of the hourly wage: log(wage) = 0.417 - 0.238female + 0.17educ + 0.0232 exper - 0.00058exper? +0.0295tenure – 0.00059tenure? female 1 if the person is female, and 0 if the person is male educ = level of education, in years exper = level of expertise, in years tenure = duration of tenure, in years Given that the above regression model has 313 observations, then its degrees of freedom is equal to For the same levels of education, expertise, and tenure, women earn exactly % less compared to men, holding other factors fixed.
The following data gives the approximate population of the world for selected years from 1850 until2000. Year 1850 1900 1950 1980 2000 Population 1.3 (billions) 1.6 3 4.4 Assume that the population growth can be modeled with an exponential function p = bemx where x is the year and p is the population in billions. Use linearleast-squares regression to determine the constants b and m for which the function best fits the data. Use the equation to estimate the population in the year 1970.
Comment on the following statement: "The cost function for logistic regression trained with 100 examples is always non- negative."
You are an econometrician working in the Ministry of Finance in Trinidad and Tobago and using a database consisting of 108 monthly observations on automobile accidents for Trinidad and Tobago between January 2011 and December 2019, you estimate the following model: log( totacc,) = B, + Bit + B2feb, + ßzmar.. + B12dec, + µe where totacc is the total number of accidents, t is time (measured in months), and feb,, mar,, dec, are dummy variables indicating whether time period t corresponds to the appropriate month. You obtain the following OLS results: Number of obs F( 12, Prob > F df 108 31.06 0.0000 0.7969 Source I MS 95) Model | 1.00244071 Residual | .255496765 12 .083536726 95 .00268944 R-squared Adj R-squared = 0.7712 Root MSE Total | 1.25793748 107 .011756425 - .05186 ltotacc | Coef. Std. Err. t P>It| [958 Conf. Interval] .0024274 -.0912208 .031287 .0027471 .0001611 .0244475 .0244491 17.06 0.000 .0030669 .0058479 .1283621 feb | -.0426865 mar | apr | may | jun | jul | aug | sep I oct I…