Please show all work/steps 9. Consider again the data describing the relationship between exercise and glucose levels describedin problem 4. Use the data in problem 4 to answer the following questions.a. Find the regression line.b. Interpret, if possible, the coefficients of the regression line.c. If possible, estimate the average blood glucose level for an adult who exercises two hours a day,every day of the week.d. If possible, estimate the average blood glucose level for an adult who exercises one hour a day,three days a week. 4. A question of interest is whether increasing the amount of exercise will decrease blood glucoseph levels for pre-diabetic adults and if so, in what fashion. A random sample of twenty pre-diabeticadults was obtained. The data below gives the amount of time spent exercising per week (in hours)and the person's blood glucose level (measured in mg/dL).Exercise TimeGlucose LevelsExercise TimeGlucose Level1.671554.510521503.751251.51565.952.3149982.9215171003.51291182.081455.51142.51356.5981.251544.51073.75 01277.580

- Consider again the data describing the relationship between exercise and glucose levels described in problem 4. Use the data in problem 4 to answer the following questions. a. Find the regression line. b. Interpret, if possible, the coefficients of the regression line. c. If possible, estimate the average blood glucose level for an adult who exercises two hours a day, every day of the week.

- Consider again the data describing the relationship between exercise and glucose levels described in problem 4. Use the data in problem 4 to answer the following questions. a. Find the regression line. b. Interpret, if possible, the coefficients of the regression line. c. If possible, estimate the average blood glucose level for an adult who exercises two hours a day, every day of the week.

College Algebra

1st Edition

ISBN:9781938168383

Author:Jay Abramson

Publisher:Jay Abramson

Chapter4: Linear Functions

Section: Chapter Questions

Problem 40RE: For the following exercises, consider the data in Table 5, which shows the percent of unemployed ina...

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EXERCISES The following table gives the life expectancy at birth of females born in the United States in various years from 1970 to 2010. Source: National Center for Health Statistics. Year of Birth Life Expectancy years 1970 74.7 1975 76.6 1980 77.4 1985 78.2 1990 78.8 1995 78.9 2000 79.3 2005 79.9 2010 81.0 Find the life expectancy predicted by your regression equation for each year in the table, and subtract it from the actual value in the second column. This gives you a table of residuals. Plot your residuals as points on a graph.
For the following exercises, consider the data in Table 5, which shows the percent of unemployed ina city of people 25 years or older who are college graduates is given below, by year. 40. Based on the set of data given in Table 6, calculate the regression line using a calculator or other technology tool, and determine the correlation coefficient to three decimal places.
For the following exercises, consider the data in Table 5, which shows the percent of unemployed in a city ofpeople25 years or older who are college graduates is given below, by year. 41. Based on the set of data given in Table 7, calculatethe regression line using a calculator or othertechnology tool, and determine the correlationcoefficient to three decimal places.
A sales manager collected the following data on annual sales for new customer accounts and the number of years of experience for a sample of 10 salespersons. Click on the datafile logo to reference the data. a. Choose the correct scatter diagram for these data with years of experience as the independent variable. B (NEED ANSWERS FOR B and C) b. Develop an estimated regression equation that can be used to predict annual sales given the years of experience. c. Use the estimated regression equation to predict annual sales for a salesperson with 9 years of experience (to the nearest whole number).
Question 3. Calculate and interpret the regression line for the data.
Use the following data to answer Questions a, b, and c. a) Determine the correlation coefficient between the percentage of people who get greater than 7 hours of sleep and the percentage who score in the 95th percentile on cognitive tests. b) Find the equation of the regression line for the percentage who get greater than 7 hours of sleep and the percentage who score in the 95th percentile on cognitive tests. c) What percentage of people can we anticipate score in the 95th percentile on cognitive tests in a generation where 27% get greater than 7 hours of sleep? Generation Millennials Gen X Boomers Gen Y Percentage who get greater than 7 hours of sleep (x) 41 Millennials 53 Gen X 64 Boomers 68 Gen y Percentage who score high (95th percentile) on cognitive tests (y) 39 Millennials 47 Gen X 56 Boomers 66 Gen Y
STER. 1. Wine Consumption. The table below gives the U.S. adult wine consumption, in gallons per person per year, for selected years from 1980 to 2005. a) Create a scatterplot for the data. Graph the scatterplot Year Wine below. Consumption 2.6 b) Determine what type of model is appropriate for the 1980 data. 1985 2.3 c) Use the appropriate regression on your calculator to find a Graph the regression equation in the same coordinate plane below. d) According to your model, in what year was wine consumption at a minimum? A e) Use your model to predict the wine consumption in 2008. 1990 2.0 1995 2.1 2000 2.5 2005 2.8
Listed below are the numbers of commuters and the number of parking spaces at different Metro-North railroad stations. Use technology (calculator) to help you answer the following, and round to the 3 decimal places where rounding is necessary. . a. Find the linear regression line y = a + bx. b. Are the variables positively or negatively related? c. Find and interpret r2. Make sure to include what it means specific to this data set. d. Use your regression line to make a prediction for the number of parking spaces for a station with 900 e. Identify and interpret the slope of the linear model.
Consider the following data: A. Find the equation of the regression line. B. Draw the graph of the regression equation on the scatter plot. x 1 2 3 4 5 6 7 y 15 10 20 5 25 20 35
1. An aerobic exercise instructor submitted to the shipping company the data given in the following table, which shows the recommended maximum exercise heart rate for individuals of the given ages. See table below. A. What is the sum of X? B. What is the sum of Y? C. What is the sum of Y? D. What is the sum of XY? E. What is the slope of the line? F. What is the intercept of the regression line? Аge (x years) Maximum heart rate (y beats per minute) 20 40 60 170 153 136
The following data are the monthly salaries and the grade point averages for students who obtained a bachelor's degree in business administration. GPA Monthly Salary ($) 2.6 3,600 3.4 3,900 3.6 4,300 3.2 3,800 3.5 4,200 2.9 3,900 The estimated regression equation for these data is and . Use Table 1 of Appendix B. a. Develop a point estimate of the starting salary for a student with a GPA of (to 1 decimal). b. Develop a confidence interval for the mean starting salary for all students with a GPA (to 2 decimals). ( , ) c. Develop a prediction interval for Ryan Dailey, a student with a GPA of (to 2 decimals). ( , ) d. Discuss the differences in your answers to parts (b) and (c).
Mark Price, the new productions manager for Speakers and Company, needs to find out which variable most affects the demand for their line of stereo speakers. He is uncertain whether the unit price of the product or the effects of increased marketing are the main drivers in sales and wants to use regression analysis to figure out which factor drives more demand for its particular market. Pertinent information was collected by an extensive marketing project that lasted over the past 12 years and was reduced to the data that follow: YEAR 123456789 10 11 12 UNIT SALES (THOUSANDS) 396 696 896 1,285 1,155 1,186 896 1,118 985 1,334 926 820 PRICE $/UNIT 282 219 206 210 219 209 218 206 220 206 226 236 ADVERTISING ($000) 600 829 1,118 1,413 1,214 1,285 907 1,118 708 907 708 682 a. Perform a regression analysis based on these data using Excel. (Negative values should be indicated by a minus sign. Round your answers to 4 decimal places.)