The pattern of higher rates of current alcohol use, binge alcohol use, and heavy alcohol use among full-time college students. than among others aged 18 to 22 has remained consistent since 2002. Using data from 2002 to 2008, the equations representing the percents of young adults aged 18 to 22 who used alcohol are shown below, where x represents the number of years after 2000. Enrolled in college y=-0.264x+19.598 Not enrolled y=-0.056x + 13.584 Solve this system to determine when the percent for those enrolled in college will equal that for those not enrolled. What will the percent be? COCO In the year, the percent for those enrolled in college will equal that for those not enrolled. (Round up to the nearest year.) The percent of young adults aged 18 to 22 using alcohol will be %. (Round to the nearest whole number as needed.).

The pattern of higher rates of current alcohol use, binge alcohol use, and heavy alcohol use among full-time college students. than among others aged 18 to 22 has remained consistent since 2002. Using data from 2002 to 2008, the equations representing the percents of young adults aged 18 to 22 who used alcohol are shown below, where x represents the number of years after 2000. Enrolled in college y=-0.264x+19.598 Not enrolled y=-0.056x + 13.584 Solve this system to determine when the percent for those enrolled in college will equal that for those not enrolled. What will the percent be? COCO In the year, the percent for those enrolled in college will equal that for those not enrolled. (Round up to the nearest year.) The percent of young adults aged 18 to 22 using alcohol will be %. (Round to the nearest whole number as needed.).

Algebra and Trigonometry (6th Edition)

6th Edition

ISBN:9780134463216

Author:Robert F. Blitzer

Publisher:Robert F. Blitzer

ChapterP: Prerequisites: Fundamental Concepts Of Algebra

Section: Chapter Questions

Problem 1MCCP: In Exercises 1-25, simplify the given expression or perform the indicated operation (and simplify,...

See similar textbooks

Similar questions

A real estate agent has developed a linear model for the price of a house, P, in dollars in terms of the area, A, in square feet for the homes in a certain neighborhood. The data set had areas ranging from 1,000 square feet to 4,500 square feet. Would predicting the prices of a home that is 4,900 square feet be interpolation or extrapolation? Explain. Using the model to predict the price of a 4,900 square foot home is extrapolation because 4,900 square feet is outside the range of the areas in the data. Using the model to predict the price of a 4,900 square foot home is interpolation because 4,900 square feet is inside the range of the areas in the data. Using the model to predict the price of a 4,900 square foot home is extrapolation because 4,900 square feet is inside the range of the areas in the data. Using the model to predict the price of a 4,900 square foot home is interpolation because 4,900 square feet is outside the range of the areas in the data.
Mary used a sample of 68 large U.S. cities to estimate the relationship between Crime (annual property crimes per 100,000 persons) and Income (median annual income per capita, in dollars). Her estimated regression equation was Crime = 428 + 0.050 Income. If Income decreases by 500, we would expect that Crime will Group of answer choices remain unchanged. decrease by 25. increase by 500. decrease by 50.
A marketing consultant created a linear regression model to predict the number of units sold by a client based on the amount of money spent on marketing by the client. Which of the following is the best graphic to use to evaluate the appropriateness of the model?
The arm span and foot length were both measured (in centimeters) for each of 20 students in a biology class. The computer output displays the regression analysis. Which of the following is the best interpretation of the coefficient of determination r2? About 37% of the variation in arm span is accounted for by the linear relationship formed with the foot length. About 65% of the variation in foot length is accounted for by the linear relationship formed with the arm span. About 63% of the variation in arm span is accounted for by the linear relationship formed with the foot length. About 63% of the variation in foot length is accounted for by the linear relationship formed with the arm span.
The table below shows the number of state-registered automatic weapons and the murder rate for several Northwestern states. x 11.9 8.4 6.6 3.8 2.6 2.3 2.2 0.9 y 14.2 11.1 9.6 7 6.2 6.1 5.8 5 x = thousands of automatic weaponsy = murders per 100,000 residentsThis data can be modeled by the equation y=0.85x+4.03. Use this equation to answer the following; Special Note: I suggest you verify this equation by performing linear regression on your calculator.A) How many murders per 100,000 residents can be expected in a state with 7.7 thousand automatic weapons?
Using your favorite statistics software package, you generate a scatter plot with a regression equation and correlation coefficient. The regression equation is reported as y= -71.12x + 92.48 and the r= -0.571
Data was collected on the number of pairs of shoes people own and the number of miles they walk per week. 2 = 0.38 . Then 38% of the variation in miles walked can be explained by the linear relationship between the in the number of shoes owned and the miles walked. 62% is attributable to other factors. false true
Bicycling World, a magazine devoted to cycling, reviews hundreds of bicycles throughout the year. Its "Road-Race" category contains reviews of bicycles used by riders primarily interested in racing. One of the most important factors in selecting a bicycle for racing is the weight of the bicycle. The following data show the weight (pounds) and price ($) for ten racing bicycles reviewed by the magazine. Model Weight Price Fierro 7B 17.9 2200 HX 5000 16.2 6350 Durbin Ultralight 16 8490 Schmidt 16.2 6300 WSilton Advanced 17.5 4100 bicyclette vélo 15.2 8500 Supremo Team 16.5 6200 XTC Racer 17.6 2680 D’Onofrio Pro 17.5 4500 Americana #6 13.6 8200 Use the data to develop an estimated regression equation that could be used to estimate the price for a bicycle given its weight. What is the estimated regression model? Group of answer choices y = -1512x + 30578 y = 1351x + 26327 y = -1161x + 24489 y = -1457x + 27903 y = 1341x + 25176
Range of ankle motion is a contributing factor to falls among the elderly. Suppose a team of researchers is studying how compression hosiery, typical shoes, and medical shoes affect range of ankle motion. In particular, note the variables Barefoot and Footwear2. Barefoot represents a subject's range of ankle motion (in degrees) while barefoot, and Footwear2 represents their range of ankle motion (in degrees) while wearing medical shoes. Use this data and your preferred software to calculate the equation of the least-squares linear regression line to predict a subject's range of ankle motion while wearing medical shoes, ?̂ , based on their range of ankle motion while barefoot, ? . Round your coefficients to two decimal places of precision. ?̂ = A physical therapist determines that her patient Jan has a range of ankle motion of 7.26°7.26° while barefoot. Predict Jan's range of ankle motion while wearing medical shoes, ?̂ . Round your answer to two decimal places. ?̂ = Suppose Jan's…
The accompanying table shows the percentage of employment in STEM (science, technology, engineering, and math) occupations and mean annual wage (in thousands of dollars) for 16 industries. The equation of the regression line is 31.218x +45.915. Use these data to construct a 95% prediction interval for the mean annual wage (in thousands of dollars) when the percentage of employment in STEM occupations is 14% in the industry. Interpret this interval. E Click the icon to view the mean annual wage data. Construct a 95% prediction interval for the mean annual wage (in thousands of dollars) when the percentage of employment in STEM occupations is 14% in the industry.
A random sample of 136 adults were asked to report the number of hours per week the spent on a computer and their number of years of education. The linear model equation below describes the relationship between the mean computers hours and years of education. computers == 9.12 ++ 0.8 ×× education Based on this linear model, which of the following statements is correct? a) An adult who has no years of education is expected to spend 9.12 hours per week on a computer. b) An adult who has 1 more year of education than another is expected to spend 9.12 more hours per week on a computer. c) An adult who spends 1 more hour per week on a computer than another is expected to have had 9.12 more years of education. d) An adult who spend zero hours per week on a computer is expected to have 9.12 years of education.
A biologist was recording various temperatures (in degrees Celsius) and predicting the amount of glucose produced by a plant (in milligrams). Suppose the linear equation was the following: ?̂ = 12.7 − 0.223? d) Suppose the correlation coefficient was −0.12. How would you classify the correlation between temperature and amount of glucose?