Construct a model of population flow between metropolitan and nonmetropolitan areas of a given country, given that their respective populations in 2015 were 263 million and 45 million. The probabilities are given by the following matrix. (from) (to) metro nonmetro 0.99 0.02 metro 0.01 0.98 nonmetro Predict the population distributions of metropolitan and nonmetropolitan areas for the years 2016 through 2020 (in millions, to four decimal places). (Let x, through x5 represent the years 2016 through 2020, respectively.) x₁ = x2 X3 261.27 46.73 11 259.59 48.41 11 257.96 50.04 11 256.39 51.61 11 t = = x 256.39 51.61 11 254.85 If a person was living in a metropolitan area in 2015, what is the probability that the person will be living in a metropolitan area in 2020? (Round your answer to four decimal places.) x 0.9690 eBook X5 53.15

Construct a model of population flow between metropolitan and nonmetropolitan areas of a given country, given that their respective populations in 2015 were 263 million and 45 million. The probabilities are given by the following matrix. (from) (to) metro nonmetro 0.99 0.02 metro 0.01 0.98 nonmetro Predict the population distributions of metropolitan and nonmetropolitan areas for the years 2016 through 2020 (in millions, to four decimal places). (Let x, through x5 represent the years 2016 through 2020, respectively.) x₁ = x2 X3 261.27 46.73 11 259.59 48.41 11 257.96 50.04 11 256.39 51.61 11 t = = x 256.39 51.61 11 254.85 If a person was living in a metropolitan area in 2015, what is the probability that the person will be living in a metropolitan area in 2020? (Round your answer to four decimal places.) x 0.9690 eBook X5 53.15

Functions and Change: A Modeling Approach to College Algebra (MindTap Course List)

6th Edition

ISBN:9781337111348

Author:Bruce Crauder, Benny Evans, Alan Noell

Publisher:Bruce Crauder, Benny Evans, Alan Noell

Chapter5: A Survey Of Other Common Functions

Section5.3: Modeling Data With Power Functions

Problem 6E: Urban Travel Times Population of cities and driving times are related, as shown in the accompanying...

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Urban Travel Times Population of cities and driving times are related, as shown in the accompanying table, which shows the 1960 population N, in thousands, for several cities, together with the average time T, in minutes, sent by residents driving to work. City Population N Driving time T Los Angeles 6489 16.8 Pittsburgh 1804 12.6 Washington 1808 14.3 Hutchinson 38 6.1 Nashville 347 10.8 Tallahassee 48 7.3 An analysis of these data, along with data from 17 other cities in the United States and Canada, led to a power model of average driving time as a function of population. a Construct a power model of driving time in minutes as a function of population measured in thousands b Is average driving time in Pittsburgh more or less than would be expected from its population? c If you wish to move to a smaller city to reduce your average driving time to work by 25, how much smaller should the city be?
The table to the right represents the annual percentage of smartphones sold in a certain region in 2011, 2012, and 2013 (projected). a. What conclusions can you reach about the market for smartphones in 2011, 2012, and 2013? b. What differences are there in the market for smartphones in 2011, 2012, and 2013? Type Type 1 Type 2 Type 3 Type 4 Type 5 Type 6 2011 2012 52% 65% 18% 16% 1% 6% 13% 7% 13% 3% 4% 2% a. Select all that apply. A. Type 3 had the second largest percentage of smartphones sold in all three years. B. Type 2 had the second largest percentage of smartphones sold in all three years. C. Type 6 had the smallest or close to the smallest percentage of smartphones sold in all three years. D. Type 1 had the largest percentage of smartphones sold in all three years. E. Type 5 had the smallest or close to the smallest percentage of smartphones sold in all three years. 2013 54% 20% 18% 4% 2% 2%
Can you please help with this question? Life insurance companies are keenly interested in predicting how long their customers will live, because their premiums and profitability depend on such numbers. An actuary for one insurance company gathered data from 100 recently deceased male customers. He recorded the age at death of the customer plus the ages at death of his mother and father, the mean ages at death of his grandmothers and the mean ages at death of his grandfathers. These data are recorded in columns 1 to 5 respectively, in file LONGEVITY.xlsx. a Perform a multiple regression analysis on these data. b Interpret the coefficient estimates and their signs. c Test for the significance of all the independent variable coefficients. (alpha = 0.05) d Is the model likely to be useful in predicting men’s longevity? e Are the required conditions satisfied? Justify your answer. f Predict the longevity of a man whose parents lived to the age of 70, whose grandmothers averaged 80 years and…
The electric power consumed each month by a chemical plant is thought to be related to the average ambient temperature x1, the number of days in the month x2, the average product purity X3, and the tons of product produced X4. The past year's historical data are available and are presented in the following table. ... 240 236 290 274 24 25 31 45 91 90 88 87 91 94 100 95 21 110 88 94 99 24 60 65 72 25 25 26 301 316 300 296 267 276 25 25 24 25 25 23 87 86 88 91 90 89 97 96 110 105 80 84 75 60 288 261 50 38 100 98 (a) Fit a multiple linear regression model using the above data set (b) Predict power consumption for a month in which x1 = 75°F, X2 = 24 days, X3 = 90%, and X4 = 98 tons.
When wildebeest are more abundant, they graze the grass more heavily, so there are fewer fires and moretrees grow. Lions feed more successfully when there are more trees, so the lion population increases. Thedata on one part of this cycle, wildebeest abundance (in thousands of animals) and the percent of the grassarea that burned in the same year are on the spreadsheet. To what extent do these data support the claim that more wildebeest reduce the percent of grasslands that burn? How rapidly does burned area decrease as the number of wildebeest increases? To answer these questions, create a scatterplot and add the regression line. Describe the scatterplot. Interpret the slope and intercept in context of the problem. Does the intercept explanation make sense in the real world? Find the correlation coefficient and coefficient of determination. What do they tell us about the relationship between number of wildebeest and percent of grassland burned? Beest (in 1000s) Percent Burned…
You run a gizmo factory. The following data shows the daily output of your factory for the past year. 488 462 492 480 470 495 467 476 494 474 496 500 495 499 494 481 477 481 468 470 476 479 478 471 484 492 477 488 489 488 499 491 486 477 503 484 532 515 476 493 475 484 498 476 483 506 467 444 466 464 478 454 473 465 491 460 470 483 446 471 529 493 471 468 505 467 506 501 492 477 478 486 454 492 500 471 467 477 485 472 475 493 447 508 478 446 500 496 494 485 493 477 511 487 498 512 446 489 458 461 461 486 501 484 470 477 484 464 491 470 478 480 491 462 483 476 476 483 490 457 511 517 478 499 479 467 500 484 487 473 472 481 458 471 495 451 485 491 482 470 507 498 491 433 488 501 496 446 482 477 467 462 473 466 503 461 511 524 459 487 474 468 485 497 457 448 460 493 494 463 480 475 479 506 485 460 494 480 480 463 510 490 477 470 494 483 474 466 471 475 487 502 502 482 442 468 490 511 499 487 472 458 469 463 498 470 501 486 461 499 497 493 445…
The table to the right represents the annual percentage of smartphones sold in a certain region in 2011, 2012, and 2013 (projected). a. What conclusions can you reach about the market for smartphones in 2011, 2012, and 2013? b. What differences are there in the market for smartphones in 2011, 2012, and 2013? a. Select all that apply. A. Type 6 had the smallest or close to the smallest percentage of smartphones sold in all three years. B. Type 3 had the second largest percentage of smartphones sold in all three years. C. Type 1 had the largest percentage of smartphones sold in all three years. D. Type 5 had the smallest or close to the smallest percentage of smartphones sold in all three years. E. Type 2 had the second largest percentage of smartphones sold in all three years. Туре Type 1 Type 2 Type 3 Type 4 Type 5 Type 6 2011 44% 21% 21% 10% 1% 3% 2012 69% 16% 4% 6% 3% 2% 2013 63% 17% 1% 3% 13% 3%
The owner of a chain of mini-markets wants to compare the sales performance of two of her stores, Store 1 and Store 2. Sales can vary considerably depending on the day of the week and the season of the year, so she decides to eliminate such effects by making sure to record each store's sales on the same 10 days, chosen at random. She records the sales (in dollars) for each store on these days, as shown in the table below. Based on these data, can the owner conclude, at the 0.05 level of significance, that the mean daily sales of the two stores differ? Answer this question by performing a hypothesis test regarding μd (which is μ with a letter "d" subscript), the population mean daily sales difference between the two stores. Assume that this population of differences (Store 1 minus Store 2) is normally distributed. Perform a two-tailed test. Then complete the parts below. Carry your intermediate computations to three or more decimal places and round your answers as specified. (If…