Elementary Statistics ( 3rd International Edition ) Isbn:9781260092561
3rd Edition
ISBN: 9781259969454
Author: William Navidi Prof.; Barry Monk Professor
Publisher: McGraw-Hill Education
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Chapter 4, Problem 13CQ
A sample of students was studied to determine the relationship between sleeping habits and classroom performance. The least-squares regression line for predicting the score on a standardized exam from hours of sleep was computed to be
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A regression was run to determine if there is a relationship between the happiness index (y) and lifeexpectancy in years of a given country (x). The results of the regression were: y^=a+bx ; a=-0.423 ,b=0.07
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A group of 13 healthy children and adolescents participated in a phycological study designed to analyze the relationship between age and average total sleep time (ATST). To obtain a measure for ATST (in minutes), recordings were taken on each subject on three consecutive nights and then averaged. Results are provided to you in Sleep&Age.xlsx Download Sleep&Age.xlsx file. (2 points) Determine the least-squares regression line for predicting average total sleep time using age. (2 points) Make a scatter plot of the data with ATST on the y-axis (vertical axis) and Age on the x-axis (horizontal axis) with least squares regression line overlaid on the top (i.e.: obtain the fitted line plot). Make sure to attach the plot below. (7 points) Check the assumptions for the simple linear regression. Attach any plots you used check the assumptions and comment on them. (7 points) We want to see if the average sleep time decreases as the children grow older. Write the appropriate null and…
Chapter 4 Solutions
Elementary Statistics ( 3rd International Edition ) Isbn:9781260092561
Ch. 4.1 - In Exercises 9-12, fill in each blank with the...Ch. 4.1 - In Exercises 9-12, fill in each blank with the...Ch. 4.1 - In Exercises 9-12, fill in each blank with the...Ch. 4.1 - In Exercises 9-12, fill in each blank with the...Ch. 4.1 - Prob. 13ECh. 4.1 - Prob. 14ECh. 4.1 - In Exercises 13-16, determine whether the...Ch. 4.1 - In Exercises 13-16, determine whether the...Ch. 4.1 - In Exercises 17-20, compute the correlation...Ch. 4.1 - In Exercises 17-20, compute the correlation...
Ch. 4.1 - In Exercises 17-20, compute the correlation...Ch. 4.1 - In Exercises 17-20, compute the correlation...Ch. 4.1 - In Exercises 21-24, determine whether the...Ch. 4.1 - In Exercises 21-24, determine whether the...Ch. 4.1 - In Exercises 21-24, determine whether the...Ch. 4.1 - In Exercises 21-24, determine whether the...Ch. 4.1 - In Exercises 25-30, determine whether the...Ch. 4.1 - In Exercises 25-30, determine whether the...Ch. 4.1 - In Exercises 25-30, determine whether the...Ch. 4.1 - In Exercises 25-30, determine whether the...Ch. 4.1 - In Exercises 25-30, determine whether the...Ch. 4.1 - In Exercises 25-30, determine whether the...Ch. 4.1 - Price of eggs and milk: The following table...Ch. 4.1 - Government funding: The following table presents...Ch. 4.1 - Pass the ball: The following table lists the...Ch. 4.1 - Carbon footprint: Carbon dioxide (CO2) is produced...Ch. 4.1 - Foot temperatures: Foot ulcers are a common...Ch. 4.1 - Mortgage payments: The following table presents...Ch. 4.1 - Blood pressure: A blood pressure measurement...Ch. 4.1 - Prob. 38ECh. 4.1 - Police and crime: In a survey of cities in the...Ch. 4.1 - Age and education: A survey of U.S. adults showed...Ch. 4.1 - Whats the correlation? In a sample of adults, the...Ch. 4.1 - Prob. 42ECh. 4.1 - Changing means and standard deviations: A small...Ch. 4.2 - In Exercises 5-7, fill in each blank with the...Ch. 4.2 - In Exercises 5-7, fill in each blank with the...Ch. 4.2 - In Exercises 5-7, fill in each blank with the...Ch. 4.2 - Prob. 8ECh. 4.2 - Prob. 9ECh. 4.2 - Prob. 10ECh. 4.2 - Prob. 11ECh. 4.2 - Prob. 12ECh. 4.2 - In Exercises 13-16, compute the least-squares...Ch. 4.2 - In Exercises 13-16, compute the least-squares...Ch. 4.2 - In Exercises 13-16, compute the least-squares...Ch. 4.2 - In Exercises 13-16, compute the least-squares...Ch. 4.2 - Compute the least-squares regression he for...Ch. 4.2 - Compute the least-squares regression he for...Ch. 4.2 - In a hypothetical study of the relationship...Ch. 4.2 - Assume in a study of educational level in years...Ch. 4.2 - Price of eggs and milk: The following table...Ch. 4.2 - Government funding: The following table presents...Ch. 4.2 - Pass the ball: The following table lists the...Ch. 4.2 - Carbon footprint: Carbon dioxide (CO2) is produced...Ch. 4.2 - Foot temperatures: Foot ulcers are a common...Ch. 4.2 - Mortgage payments: The following table presents...Ch. 4.2 - Blood pressure: A blood pressure measurement...Ch. 4.2 - Butterfly wings: Do larger butterflies live...Ch. 4.2 - Interpreting technology: The following display...Ch. 4.2 - Interpreting technology: The following display...Ch. 4.2 - Interpreting technology: The following MINITAB...Ch. 4.2 - Interpreting technology: The following MINITAB...Ch. 4.2 - Prob. 33ECh. 4.2 - Prob. 34ECh. 4.2 - Least-squares regression line for z-scores: The...Ch. 4.3 - In Exercises 5-10, fill in each blank with the...Ch. 4.3 - In Exercises 5-10, fill in each blank with the...Ch. 4.3 - In Exercises 5-10, fill in each blank with the...Ch. 4.3 - In Exercises 5-10, fill in each blank with the...Ch. 4.3 - In Exercises 5-10, fill in each blank with the...Ch. 4.3 - Prob. 10ECh. 4.3 - Prob. 11ECh. 4.3 - In Exercises 11-14, determine whether the...Ch. 4.3 - Prob. 13ECh. 4.3 - In Exercises 11-14, determine whether the...Ch. 4.3 - For the following data set: Compute the...Ch. 4.3 - For the following data set: Compute the...Ch. 4.3 - For the following data set: Compute the...Ch. 4.3 - For the following data set: Compute the...Ch. 4.3 - Prob. 19ECh. 4.3 - Prob. 20ECh. 4.3 - Prob. 21ECh. 4.3 - Prob. 22ECh. 4.3 - Hot enough for you? The following table presents...Ch. 4.3 - Presidents and first ladies: The presents the ages...Ch. 4.3 - Mutant genes: In a study to determine whether the...Ch. 4.3 - Imports and exports: The following table presents...Ch. 4.3 - Energy consumption: The following table presents...Ch. 4.3 - Cost of health care: The following table presents...Ch. 4.3 - Prob. 29ECh. 4.3 - Prob. 30ECh. 4.3 - Prob. 31ECh. 4.3 - Transforming a variable: The following table...Ch. 4.3 - Prob. 33ECh. 4.3 - Prob. 34ECh. 4 - Compute the correlation coefficient for the...Ch. 4 - The number of theaters showing the movie Monsters...Ch. 4 - Use the data in Exercise 2 to compute the...Ch. 4 - A scatterplot has a correlation of r=1. Describe...Ch. 4 - Prob. 5CQCh. 4 - Prob. 6CQCh. 4 - Use the least-squares regression line computed in...Ch. 4 - Use the least-squares regression line computed in...Ch. 4 - Prob. 9CQCh. 4 - A scatterplot has a least-squares regression line...Ch. 4 - Prob. 11CQCh. 4 - Prob. 12CQCh. 4 - A sample of students was studied to determine the...Ch. 4 - In a scatter-plot; the point (-2, 7) is...Ch. 4 - The correlation coefficient for a data set is...Ch. 4 - Prob. 1RECh. 4 - Prob. 2RECh. 4 - Hows your mileage? Weight (in tons) and fuel...Ch. 4 - Prob. 4RECh. 4 - Energy efficiency: A sample of 10 households was...Ch. 4 - Energy efficiency: Using the data in Exercise 5:...Ch. 4 - Prob. 7RECh. 4 - Prob. 8RECh. 4 - Prob. 9RECh. 4 - Prob. 10RECh. 4 - Baby weights: The average gestational age (time...Ch. 4 - Commute times: Every morning, Tania leaves for...Ch. 4 - Prob. 13RECh. 4 - Prob. 14RECh. 4 - Prob. 15RECh. 4 - Describe an example which two variables are...Ch. 4 - Two variables x and y have a positive association...Ch. 4 - Prob. 3WAICh. 4 - Prob. 4WAICh. 4 - Prob. 5WAICh. 4 - Prob. 6WAICh. 4 - Prob. 7WAICh. 4 - Prob. 8WAICh. 4 - Prob. 9WAICh. 4 - The following table, reproduced from the chapter...Ch. 4 - Prob. 2CSCh. 4 - Prob. 3CSCh. 4 - Prob. 4CSCh. 4 - Prob. 5CSCh. 4 - Prob. 6CSCh. 4 - Prob. 7CSCh. 4 - Prob. 8CSCh. 4 - Prob. 9CSCh. 4 - Prob. 10CSCh. 4 - Prob. 11CSCh. 4 - Prob. 12CSCh. 4 - Prob. 13CSCh. 4 - If we are going to use data from this year to...Ch. 4 - Prob. 15CS
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- If your graphing calculator is capable of computing a least-squares sinusoidal regression model, use it to find a second model for the data. Graph this new equation along with your first model. How do they compare?arrow_forwardA frequent flyer was interested in the relationship between dollars spent on flying and the distance flown. She sampled 20 frequent flyers of a certain airline. She collected the number of miles flown in the previous year and the total amount of money the flyer spent. A regression line of distance flown on money spent was fit to the data: \hat y = 24000 + 10xy^=24000+10x. A person who spent $1000 is predicted to have flown:arrow_forwardthe accompanying data represent the weights of various domestic cars and their gas mileages in the city. the linear correlation coefficient between the weight of a car and its miles per gallon in the city is r= -0.972. The least- squares regression line treating weight as the explanatory variable and miles per gallon as the response variable is ^y =-0.0070x+44.4405 what proportion of the variability in miles per gallon is explained by the relation between weight of the car and miles per gallon? interpret the coefficient of determination % of the variance in is by the linear modelarrow_forward
- A frequent flyer was interested in the relationship between dollars spent on flying and the distance flown. She sampled 20 frequent flyers of a certain airline. She collected the number of miles flown in the previous year and the total amount of money the flyer spent. A regression line of distance flown on money spent was fit to the data: y^=24000+10x. One person in the sample spent $3000 and flew 42,567 miles. What would be the residual (or error) in the predicted distance this person would fly?arrow_forwardThe table below gives the number of hours ten randomly selected students spent studying and their corresponding midterm exam grades. Using this data, consider the equation of the regression line, y^=b0+b1x, for predicting the midterm exam grade that a student will earn based on the number of hours spent studying. Keep in mind, the correlation coefficient may or may not be statistically significant for the data given. Remember, in practice, it would not be appropriate to use the regression line to make a prediction if the correlation coefficient is not statistically significant. Hours Studying 0 1 2 2.5 3 3.5 4 4.5 5.5 6 Midterm Grades 61 64 65 66 71 79 84 88 94 98 Summation Table x y xy x2 y2 Student 1 0.0 61 0.0 0.00 3721 Student 2 1.0 64 64.0 1.00 4096 Student 3 2.0 65 130.0 4.00 4225 Student 4 2.5 66 165.0 6.25 4356 Student 5 3.0 71 213.0 9.00 5041 Student 6 3.5 79 276.5 12.25 6241 Student 7 4.0 84 336.0 16.00 7056 Student 8 4.5 88 396.0 20.25 7744…arrow_forwardhe table below gives the list price and the number of bids received for five randomly selected items sold through online auctions. Using this data, consider the equation of the regression line, yˆ=b0+b1xy^=b0+b1x, for predicting the number of bids an item will receive based on the list price. Keep in mind, the correlation coefficient may or may not be statistically significant for the data given. Remember, in practice, it would not be appropriate to use the regression line to make a prediction if the correlation coefficient is not statistically significant. Price in Dollars 23 26 37 41 47 Number of Bids 1 2 4 5 6 Table Copy Data Step 1 of 6 : Find the estimated slope. Round your answer to three decimal places.arrow_forward
- The table below gives the number of hours five randomly selected students spent studying and their corresponding midterm exam grades. Using this data, consider the equation of the regression line, y^=b0+b1x, for predicting the midterm exam grade that a student will earn based on the number of hours spent studying. Keep in mind, the correlation coefficient may or may not be statistically significant for the data given. Remember, in practice, it would not be appropriate to use the regression line to make a prediction if the correlation coefficient is not statistically significant. Hours Studying 1 2 4 5 6 Midterm Grades 67 68 70 79 87 Summation Table x y xy x2 y2 Student 1 1 67 67 1 4489 Student 2 2 68 136 4 4624 Student 3 4 70 280 16 4900 Student 4 5 79 395 25 6241 Student 5 6 87 522 36 7569 Sum 18 371 1400 82 27823 Step 1 of 6: Find the estimated slope. Round your answer to three decimal places. Step 2 of 6: Find the estimated y-intercept. Round your…arrow_forwardResearchers found a positive assodation between the students' performance in STAT 1000 and their first-year cumulative college GPA. Furthermore, STAT 1000 course GPA explained 62% of the variation in students'first-year cumulative college GPA. The summarized data is given below: Mean STAT 1000 GPA = 25 Std. dev. - 0.21 Mean cumulative College GPA = 325 Std. dev, = 03 The slope and intercept of the least squares regression line for predicting first-year college GPA from STAT 1000 scores are, respectively. Oa Slope 055 Intercept 1.88 Ob Slope = 1.12 Intercept 1.88 Oc Slope = 1.12 Intercept 044 Od. Slope 044 Intercept 055 Oe Slope- 1.88 Intercept 055arrow_forwardA frequent flyer was interested in the relationship between dollars spent on flying and the distance flown. She sampled 20 frequent flyers of a certain airline. She collected the number of miles flown in the previous year and the total amount of money the flyer spent. A regression line of distance flown on money spent was fit to the data: \hat y = 24000 + 10xy^=24000+10x. One person in the sample spent $2000 and flew 42,567 miles. What would be the residual (or error) in the predicted distance this person would fly?arrow_forward
- The table below gives the list price and the number of bids received for five randomly selected items sold through online auctions. Using this data, consider the equation of the regression line, yˆ=b0+b1xy^=b0+b1x, for predicting the number of bids an item will receive based on the list price. Keep in mind, the correlation coefficient may or may not be statistically significant for the data given. Remember, in practice, it would not be appropriate to use the regression line to make a prediction if the correlation coefficient is not statistically significant. Price in Dollars 23 25 32 36 40 Number of Bids 2 4 5 8 10arrow_forwardThe table below gives the list price and the number of bids received for five randomly selected items sold through online auctions. Using this data, consider the equation of the regression line, yˆ=b0+b1xy^=b0+b1x, for predicting the number of bids an item will receive based on the list price. Keep in mind, the correlation coefficient may or may not be statistically significant for the data given. Remember, in practice, it would not be appropriate to use the regression line to make a prediction if the correlation coefficient is not statistically significant. Price in Dollars 22 33 35 40 45 Number of Bids2 3 4 6 7 Step 1 of 6: Find the estimated slope. Round your answer to three decimal places. Step 2: Find the estimated y-intercept. Round your answer to three decimal places. Step 3 of 6:Find the estimated value of y when x=33. Round your answer to three decimal places. Step 4 of 6:Determine the value of the dependent variable y^ at x=0 Step 5 of…arrow_forwardThe table below gives the list price and the number of bids received for five randomly selected items sold through online auctions. Using this data, consider the equation of the regression line, yˆ=b0+b1xy^=b0+b1x, for predicting the number of bids an item will receive based on the list price. Keep in mind, the correlation coefficient may or may not be statistically significant for the data given. Remember, in practice, it would not be appropriate to use the regression line to make a prediction if the correlation coefficient is not statistically significant. Step 2: Find the estimated y-intercept. Round your answer to three decimal places. Step 3 of 6:Find the estimated value of y when x=33. Round your answer to three decimal places. Step 4 of 6:Determine the value of the dependent variable y^ at x=0 Step 5 of 6:Determine if the statement "Not all points predicted by the linear model fall on the same line" is true or false. Step 6 of 6:Find the value of the coefficient of…arrow_forward
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