The table shows the number of sales of scooters y, in thousands, x months after they are introduced on the market. Use regression to find a logistic function f thatmodels this data. Round the constants to the nearest hundredth.x (months)3121518y (thousands of scooters) 47103513101015071560.....1631.75O A. f(x) =- 0.50x1+ 217.92 e1709.72O B. f(x) =- 0.53x1 +319.46 e1704.94Oc. f(x) =-0.57x1+ 530.84 e1724.99O D. f(x) =- 0.50x1+ 242.01 e

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Answered: The table shows the number of sales of…

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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Run a regression of the logarithm of average hourly earnings, ln(wage) on educ, exper, exper2, female, married and numdep. Discuss the statistical significance of each of the slope coefficients and interpret the coefficient on female and married.
The table below shows the value, V, of an investment (in dollars) n years after 1992. 1 3 7 4120 4283 4919 12 14 5874 6050 n V(n) 6663 Use your calculator to determine the exponential regression equation that models the set of data above. Round the "a" value to the nearest whole number, and round the "b" value to three decimals. Use the indicated variables and proper function notation. V(n) Based on the your regression model, what is the percent increase per year? The value of this investment is increasing at a rate of % per year. Based the your regression model, what is value of this investment in the year 1992? Round your answer to the nearest dollar. The value of this investment is $ in the year 1992. Based on the your regression model, what is value of this investment in the year 2002? Round your answer to the nearest dollar. The value of this investment is S in the year 2002.
Data was recorded for the temperature, in degrees Celsius, of a cup of coffee over a 30-minute period. Given the regression equation, In(Temp) = 4.20 0.023(Time), what is the predicted temperature after 3 minutes? 33.45 °C 62.24 °C 65.17 °C 66.69 °C
Annabelle records the length, in inches, of a colored pencil over time. Review the table showing her data. Time (Weeks) Length (Inches) 0 7.5 2 7.5 4 7 5 6 7 2 8 1 10 0.5 12 0.25 13 0.2 Which description best fits the regression model for the data? logistic equation of the form y = , witha = 7.576, b = 0.001, and c = –1.107 logistic equation of the form y = , witha = 0.001, b = –1.107, and c = 7.576 trigonometric equation of the form y = a sin(bx + c) + d, with a = 4.15, b = 0.34, c = 497.43, and d = 3.80 trigonometric equation of the form y = a sin(bx + c) + d, with a = –4.15, b = –0.34, c = –497.43, and d = 3.80
For this exercise, round all regression parameters to three decimal places.The following table shows national health care costs, measured in billions of dollars. Date 1970 1980 1990 2000 2010 Costs inbillions 75 253 714 1353 2570 (a) Plot the data. (Let t be years since 1970 and H the costs in billions of dollars.) b) Find an exponential function that approximates the data for health care costs. (Let t be years since 1970 and H the costs in billions of dollars.) H = 3120.372 × 0.916t H = 993.002 × 1.002t H = 94.414 × 1.091t H = 2560.371 × 0.872t H = 72.438 × 1.143t (c) By what percent per year were national health care costs increasing during the period from 1970 through 2010? (Use the model found in part (b). Round your answer to one decimal place.) %(d) Use functional notation to express how much money was spent on health care in the year 2015. (Let t be years since 1970.) H Estimate that value. (Use the model found in part (b). Round your answer to…
Nitrogen dioxide, NO₂, is an air pollutant resulting from burning fossil fuels in cars and power plants. The following table shows the decrease in the national average of the concentration in parts per billion (ppb) of NO₂ for selected years. Year 2014 40.8107 2015 2016 2017 2018 39.7886 38.7972 37.8209 36.8752 NO₂ (ppb) Use exponential regression to find an exponential decay function for the concentration, C, of NO₂ t years after 2014. Round constants to the nearest ten-thousandth. c-40.8114 (0.9750) Use the model to predict the concentration of NO₂ (in ppb) in 2045. Round to the nearest whole number. 21 x ppb
The table shows the yield (in bushels per acre) and the total production (in millions of bushels) for corn in a country for selected years since 1950. Let x represent years since 1900. Find a logarithmic regression model (y = a +b ln x) for the yield. Estimate the yield in 2023. Year 1950 1960 Yield 50 37 60 1970 1980 60 84 93 70 80 1990 90 116 2000 2010 100 136 110 157 The regression model is y =+( In x. (Round to one decimal place as needed.) bushels/acre. The estimated yield in 2023 is (Round to the nearest integer.)
Nitrogen dioxide, NO2, is an air pollutant resulting from burning fossil fuels in cars and power plants. The following table shows the decrease in the national average of the concentration in parts per billion (ppb) of NO2 for selected years. Year 2014 2015 2016 2017 2018 NO2 (ppb) 40.8107 39.7886 38.7972 37.8209 36.8752 Use exponential regression to find an exponential decay function for the concentration, C, of NO2 t years after 2014. Round constants to the nearest ten-thousandth. C = Use the model to predict the concentration of NO2 (in ppb) in 2050. Round to the nearest whole number. ? ppb
The logistic model P(t) = 96.0235 1+0.0455-0.1864t represents the percentage of households that do not own a personal computer t years since 1983. Complete parts (a)-(d). (a) Evaluate and interpret P(0). Evaluate P(0). P(0) = 91.8 % (Round to one decimal place as needed.) Which sentence below best describes P(0)? O A. P(0) is the year all households had a personal computer. O B. O C. O D. P(0) is the year no households had a personal computer. P(0) is the percentage of households with a personal computer in 1983. P(0) is the percentage of households without a personal computer in 1983. -C
Not long ago, Car and Driver magazine ran several sports cars as fast as they could down a 1-mile runway. The data they collected on one of the cars (a Lola Champ Car) is shown below.y=y= time (sec) speed (mph) 3.1 60 5.6 100 8.6 150 9.9 161.4 15 188.3 19.7 196.2 22 200 24.2 203.3 Use logarithmic regression to find a logarithmic function that best fits this data. y= Use logistic regression to find an logistic function that best fits this data.y=

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