Concept explainers
Differential equations can be used to model disease epidemics. In the next set of problems, we examine the change of size of two sub-populations of people living in a city: individuals who are infected and individuals who are susceptible to infection. S represents the size of the susceptible population, and I represents the size of the infected population. We assume that if a susceptible person interacts with an infected person, there is a probability c that the susceptible person will become infected. Each infected person recovers from the infection at a rate r and becomes susceptible again. We consider the case of influenza, where we assume that no one dies from the disease, so we assume that the total population size of the two sub-populations is a constant number, N. The differential equations that model these population sizes are
S' = rI − cSI and
I' =cSI − rI.
Here c represents the contact rate and r is the recovery rate.
112. [T] h = 1000
Want to see the full answer?
Check out a sample textbook solutionChapter 4 Solutions
Calculus Volume 2
Additional Math Textbook Solutions
Calculus Volume 1
Introductory Statistics
Thinking Mathematically (7th Edition)
Mathematics for Elementary Teachers with Activities (5th Edition)
Calculus for Business, Economics, Life Sciences, and Social Sciences (13th Edition)
Mathematics All Around (6th Edition)
- 2. Suppose that in Example 2.27, 400 units of food A, 500 units of B, and 600 units of C are placed in the test tube each day and the data on daily food consumption by the bacteria (in units per day) are as shown in Table 2.7. How many bacteria of each strain can coexist in the test tube and consume all of the food? Table 2.7 Bacteria Strain I Bacteria Strain II Bacteria Strain III Food A 1 2 0 Food B 2 1 3 Food C 1 1 1arrow_forwardRecall that the general form of a logistic equation for a population is given by P(t)=c1+aebt , such that the initial population at time t=0 is P(0)=P0. Show algebraically that cP(t)P(t)=cP0P0ebt .arrow_forwardThe table below gives the retail sale of electricity (in billions of kilowatt-hours) for the residential and commercial sectors of the United States. Year 2000 2003 2006 2009 2012 2015 Residential (billions of kilowatt-hours) 1,192 1,276 1,400 1,420 1,402 1,390 = Let a represent time (in years) with x 0 corresponding to 2000, and let y represent the corresponding residential sales. Find the quadratic regression equation for the data y = R(x) = ax² +bx+c with a rounded to the nearest hundredth (2 decimal places), b rounded to the nearest tenth (1 decimal place), and c rounded to the nearest integer. (See the Regression page in the Getting Started module for ways of computing a regression model.)arrow_forward
- In a simple linear regression problem, the following sum of squares are produced: Σ,-)²=200, Σ,-)² = 50, and - y)² =150 percentage of the variation in y that is explained by the variation in x is: a. 25% b. 75% c. 33% d. 50% Thearrow_forwardThe table below gives the retail sale of electricity (in billions of kilowatt-hours) for the residential and commercial sectors of the United States. Residential (billions of kilowatt-hours) 1,192 1,276 1,400 1,420 1,402 1,390 Year 2000 2003 2006 2009 2012 2015 Let a represent time (in years) with x = 0 corresponding to 2000, and let y represent the corresponding residential sales. Find the quadratic regression equation for the data y = R(x) = = ax² +bx+c = with a rounded to the nearest hundredth (2 decimal places), b rounded to the nearest tenth (1 decimal place), and c rounded to the nearest integer.arrow_forwardWw. 3.10® Expess 5 as a linear combination of and Voarrow_forward
- The output of a solar panel (photovoltaic) system depends on its size. A manufacturer states that the average daily production of its 1.5 kW system is 6.6 kilowatt hours (kWh) for Perth conditions. A consumer group monitored this 1.5 kW system in 20 different Perth homes and measured the average daily production by the systems in these homes over a one month period during October. The data is provided here. kWh 6.2, 5.8, 5.9, 6.1, 6.4, 6.3, 6.9, 5.5, 7.4, 6.7, 6.3, 6.2, 7.1, 6.8, 5.9, 5.4, 7.2, 6.7, 5.8, 6.9 1. Analyse the consumer group’s data to test if the manufacturer’s claim of an average of 6.6 kWh per day is reasonable. State appropriate hypotheses, assumptions and decision rule at α = 0.10. What conclusions would you report to the consumer group? (Hint: You will need to find Descriptive Statistics first.) 2. If 48 homes in the central Australian city of Alice Springs had this system installed and similar data was collected, in order to assess whether average daily production in…arrow_forwardPlease solve & show steps...arrow_forwardConsider the linear regression model: log{wage)-B1+B2 MATH+B3 ARABIC +e where wage is the hourly wage and MATH is you score in Math courses and ARABIC is your score in Arabic courses. You would like to test the hypothesis that MATH and ARABIC have the same effect on log(wage) against the alternative that MATH has a greater weight on log(wage) a. Formally state the null and the alternative hypotheses b. The OLS results from a sample of 300 observations are reported below log(wage)= 2.55+ 0.85 MATH+2.75 ARABIC R-square- 0.55) se (1.55) (0.035) (1.25) Test the hypothesis that you stated in (a) at 5% level given that the cov(b2,b3)=0.arrow_forward
- Clean Fossil Fuels. In the article, “Squeaky Clean Fossil Fuels” (New Scientist, Vol. 186, No. 2497, p. 26), F. Pearce reported on the benefits of using clean fossil fuels that release no carbon dioxide (CO2), helping to reduce the threat of global warming. One technique of slowing down global warming caused by CO2 is to bury the CO2 underground in old oil or gas wells, coal mines, or porous rocks filled with salt water. Global estimates are that 11,000 billion tonnes of CO2 could be disposed of underground, several times more than the likely emissions of CO2 from burning fossil fuels in the coming century. This could give the world extra time to give up its reliance on fossil fuels. The following bar chart shows the distribution of space available to bury CO2 gas underground. a. Explain why the break is found in the third bar. b. Why was the graph constructed with a broken bar?arrow_forwardIn an effort to isolate the determinants of absenteeism, Jacob estimates two different regressions, A and B, using time series data. The R2 is 0.63 for regression A and 0.91 for regression B. Which is the superior regression? Explain your reasoning.arrow_forwardSuppose that we are examining the relationship between scores on a nationwide, standardized test and performance in college. We have chosen a random sample of 96 students just finishing their first year of college, and for each student we've recorded her score on the standardized test and her grade point average for her first year in college. For our data, the least-squares regression equation relating the two variables score on this standardized test (denoted by x and ranging from 400 to 1600) and first-year college grade point average (denoted by y and ranging from 0 to 4) is y = 0.8884 +0.0020x. The standard error of the slope of this least-squares regression line is approximately 0.0016. Based on these sample results, test for a significant linear relationship between the two variables by doing a hypothesis test regarding the population slope B₁. (Assume that the variable y follows a normal distribution for each value of x and that the other regression assumptions are satisfied.)…arrow_forward
- Linear Algebra: A Modern IntroductionAlgebraISBN:9781285463247Author:David PoolePublisher:Cengage LearningAlgebra & Trigonometry with Analytic GeometryAlgebraISBN:9781133382119Author:SwokowskiPublisher:Cengage