business econ math

At low altitudes the altitude of a parachutist and time in the air are linearly related. A jump at 3,770 feet lasts 130 seconds. (A) Find a linear model relating altitude a (in feet) and time in the air t (in seconds). (B) Find the rate of change of the parachutist in the air. (C) Find the speed of the parachutist at landing. (A) Find a linear model relating altitude a (in feet) and time in the air t (in seconds). aE (Type an equation using t as the variable.)The table lists fossil fuel production as a percentage of total energy production for (A) Draw a scatter plot of the data and a graph of the model on the same selected years. A linear regression model for this data is axes. y= - 0.38x + 95.0 O A. O B. where x represents years after 1960 and y represents the corresponding percentage of oil imports. 100- 100- Fossil Fuel Production Year Production (%) 1960 95 60 60 1970 92 Years after 1960 Years after 1960 1980 86 1990 84 O C. D. 2000 80 100- 100- 60 60 Years after 1960 Years after 1960 (B) Interpret the slope of the model. The rate of change of the percentage of oil imports with respect to time is -0.38% per year. (C) Use the model to predict fossil fuel production in 2010. In 2010 fossil fuel production as a percentage of total production will be about %. (Round to one decimal place as needed.)A linear regression model for the revenue data for a company is R = 25.0t + 206 where R is total annual revenue and t is time since 1/31/02 in years. Billions of 12 months 12 months 12 months 12 months 12 months Dollars ending 1/31/02 ending ending 1/31/03 1/31/04 ending 1/31/05 ending 1/31/06 Revenue 206 234 252 282 307 Gross Profit 44 54 58 64 73 (A) Draw a scatter plot of the data and a graph of the model on the same axes. A. O B. O C. OD. 500-T 500-T 500-T 500- 10 Time since 1/31/02 (years) Time since 1/31/02 (years) Time since 1/31/02 (years) Time since 1/31/02 (years) (B) Predict the company's annual revenue for the period ending 1/31/10. R billion dollars (Round to the nearest billion dollars as needed.)The scatter plot and linear regression models for the yearly revenue of restaurants (R) and supermarkets (S) are given in the popup below. The revenue for restaurants can be modeled by the equation R = 13.5n + 180.0, and for supermarkets can be modeled by the equation S = 7.5n + 235.0, where n is the number of years since 1986 and revenue is in billions of dollars. a. Interpret the slope of each model. b. Use the model to predict the revenue of restaurants and the revenue of supermarkets in 2017. c. Estimate the first year that restaurant revenue will exceed supermarket revenue by at least 150 billion dollars. Click the icon to view the scatter plot and data table of yearly revenue. . . . a. Interpret the slope of each model. The slope of the linear regression model for restaurants shows that the revenue is increasing at a rate of 13.5 billion dollars per year. (Type an integer or a decimal.) The slope of the linear regression model for supermarkets shows that the revenue is at a rate of billion dollars per year. (Type an integer or a decimal.)