E myLoneStar Login 02 Mail-Tyrese Blackwell Path to Success-EDUC McGraw-Hill conn × slope intercept form. F b My Questions bartleb + Previous 1 23 4 5 6 7 8 9 10 Next Question 4 of 50 (1 point) 3.1 Section Exercise 31,32 (Evaluate) Question Complete the table of values for the equation Check Answer Solve It Guided Solution Show Example Link to Textbook Save for Later Submit Assignment 8:43 PM Type here to search 2/17/2019

E myLoneStar Login 02 Mail-Tyrese Blackwell Path to Success-EDUC McGraw-Hill conn × slope intercept form. F b My Questions bartleb + Previous 1 23 4 5 6 7 8 9 10 Next Question 4 of 50 (1 point) 3.1 Section Exercise 31,32 (Evaluate) Question Complete the table of values for the equation Check Answer Solve It Guided Solution Show Example Link to Textbook Save for Later Submit Assignment 8:43 PM Type here to search 2/17/2019

Linear Algebra: A Modern Introduction

4th Edition

ISBN:9781285463247

Author:David Poole

Publisher:David Poole

Chapter4: Eigenvalues And Eigenvectors

Section4.6: Applications And The Perron-frobenius Theorem

Problem 70EQ

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Recall 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 .
Determine the best (according to sum-of-squares-measure) curve y = Axb, through the dataabove.Transformed equation ln(y) = ln(A) + b ln(x)orY = a + bX.
Consider the population model dP/dt = 2P −P^250 for a species of fish in a lake. Suppose it isdecided that fishing will be allowed, but it is unclear how many fishing licenses should beissued. Suppose the average catch of a fisherman with a license is 3 fish per year (these arehard to catch fish). What is the largest number of licenses that can be issued if the fish areto have a chance to survive in the lake?
Period 1 2 3 4Data 32 14 41 30 a. Make an exponential smoothing forecast for periods 2 through 5, with twovalues of α, 0.05 and 0.60, and an assumed forecast for period 1 of 30.b. Compute the MAD for each of the forecasts.c. Which value of α should you choose?
Denote by M0 the logistic model with only an intercept term and by M1 the model that also has x as a linear predictor. Denote the maximized log likelihood values by L0 for M0, L1 for M1, and Ls for the saturated model. Recall that G2 (Mi ) = −2(Li − Ls ), i = 0, 1. a. Fit M0 and M1 for each data file. Report L0 and L1 (or −2L0 and −2L1) in each case. Note that they do not depend on the form of data entry. Please see image for data
Suppose a new magazine initially sells 600 copies per month. Research indicates that a vigorous advertising campaign could increase sales by 40% each month if our market were unlimited. But research also indicates that magazine sales in our area are unlikely to exceed 2400 per month. Make a logistic model of projected magazine sales. a) N = 600 / 1+3e^-0.336t b) N = 2400 / 1+3e^-0.336t c) N = 40 / 1+3e^-0.336t d) N = 2400 / 1+40e^-0.336t
Consider an equation to explain salaries of CEOs in terms of annual firm sales, return on equity (roe in percentage form), and return on the firm’s stock (ros, in percentage form): log (salary) = β0+ β1 log(sales) + β2roe + β3ros – u. 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. Using the data in CEOSAL1, the following equation was obtained by OLS: log(salary) ˆ = 4.32 + .280 log(sales) + .0174 roe + .00024 ros (.32) (.035) (.0041) (.00054) n = 209, R2 = .283 By what percentage is salary predicted to increase if ros increases by 50 points? Does ros have a practically large effect on salary? Test the null hypothesis that ros has no effect on salary against the alternative that ros has a positive effect. Carry out…
Consider an equation to explain salaries of CEOs in terms of annual firm sales, return on equity (roe in percentage form), and return on the firm’s stock (ros, in percentage form): log (salary) = β0+ β1 log(sales) + β2roe + β3ros – u. 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. Using the data in CEOSAL1, the following equation was obtained by OLS: = 4.32 + .280 log(sales) + .0174 roe + .00024 ros (.32) (.035) (.0041) (.00054) n = 209, R2 = .283 By what percentage is salary predicted to increase if ros increases by 50 points? Does ros have a practically large effect on salary? Test the null hypothesis that ros has no effect on salary against the alternative that ros has a positive effect. Carry out the test at the 10% significance…
Construct a model for the number of cats, y, after x months that make use of the following assumptions: 1. It begins with two cats – one female and one male, both unneutered. 2. Each litter is composed of 4 kittens – 3 males and 1 female. 3. It takes four months before a new generation of cats is born. 4. No cat dies (all are healthy) and no new cats are introduced.
Find the moment generating function for Y. c=4
Suppose that a population develops according to the logistic equation dP/dt= 0.06P − 0.0006P2 where t is measured in weeks. (a) What is the carrying capacity? M = What is the value of k? k = (b) A direction field for this equation is shown. Where are the slopes close to 0? (Enter your answers as a comma-separated list.) P = Where are they largest? (Enter your answers as a comma-separated list.) P = Which solutions are increasing? (Enter your answer using interval notation.) P0 Which solutions are decreasing? (Enter your answer using interval notation.) P0 (d) What are the equilibrium solutions? (Enter your answers as a comma-separated list.) P =
Sam was 28 inches tall on her first birthday, 50 inches tall on her 8th, and 62 inches tall on her 14th. (a) Let t represent Sam's age in years, and let h represent her height in inches. Determine the values of M, A, and k for a logistic model, h(t) = M / ( 1+ Ae-kt) , that fits the given height data. (b) What is Sam's theoretical long-term expected height? That is, what is limt➔∞h(t)? (c) At what age does the model predict that Sam will reach 95% of her expected maximum height?