10. Find a stable age distribution vector for a population with age transition matrix04A-[1]=016

Given that A=.We will find eigen values of A and then corresponding to the positive eigen value ,we…

Answered: . Find a stable age distribution vector…

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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Example 5.1.7 A car-hire firm has two cars, which are hired out everyday. The number of demands for a car on each day is distributed as a Poisson distribution with mean 1.5. Calculate the proportion of days on which neither car is used and the proportion of days on which a demand cannot be met due to lack of cars [e15 = 0.2231].
QUESTION 14 CardioGood Fitness Treadmill Products Data The market research team at CardioGood Fitness retail store wants to investigate whether there are differences across the product lines of treadmills sold; TM195 (low-end model), TM498 (medium grade model) and TM798 (high-end model) with respect to customer characteristics. The team decides to collect data on individuals who purchased a treadmill at a CardioGood Fitness retail store during the past three months. One question the team is interested in is if there is a difference in the miles of intended use of the treadmill between males and Females. t-Test: Two-Sample Assuming Unequal Variances MaleMiles Female Miles Mean 112.81 89.99 Variance 2991.43 2006.76 Observations 104 76 Hypothesized Mean Difference df 176 t Stat 3.0732 P(T<=t) one-tail 0.0012 t Critical one-tail 1.6536 P(T<=t) two-tail 0.0025 t Critical two-tail 1.9735 In the essay box, clearly answer questions 1-5 below: 1. Is this conducting a one-sided test or a…
1. Consider the following regression model y = x3 + u. (1) Let 3 denote the Ordinary Least Squares (OLS) estimator of B. The so-called Gauss- Markov assumptions are: • MLR.1: The true model in the population is given by (1). • MLR.2: We have a random sample of n observations {(ri, Yi), i = 1, 2, .., n} following the population model in (1). .... • MLR.3: No one explanatory variable can be written as a linear combination of the remaining explanatory variables that is, there is no perfectcollinearity. • MLR.4: In the population, the error u has an expected value of zero given any values of the explanatory variables, that is Elu|x] = 0. • MLR.5: In the population, the error u has the same variance given any values of the explanatory variables, that is Var[u|x] = o? , an unknown finite, positive constant. In the following scenarios, state whether 3 is an unbiased and consistent estimator of 3, and provide a brief justification for your answer in each case - but no formal mathematical…
Find any critical numbers of the given functions 1. f(x) = x* - 4x2 2. g(x) = 4x x2+1 3. h(x) = sin²x + cosx ,0 < x< 2n
What are the difficulties in estimating the following model? Use as much detail as possible in answering this question while considering the Gauss-Markov assumptions and OLS estimator. Economic productivity = β0 + β1Unemployment + β2Innovation + θiControls + ei Where unemployment is the average unemployment rate of a country and innovation is an index of R&D performance.

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