MATH 221 Week 8 Quiz (100% Correct Answers) | Download To Score An A.

MATH 221 Week 8 Quiz Question The table shows data collected on the relationship between the time spent studying per day and the time spent reading per day. The line of best fit for the data is y ˆ=0.16x+36.2. Assume the line of best fit is significant and there is a strong linear relationship between the variables. Studying (Minutes) Reading (Minutes) (a) According to the line of best fit, what would be the predicted number of minutes spent reading for someone who spent 67 minutes studying? Round your answer to two decimal places. The predicted number of minutes spent reading is 46 point 9 2$$46.9246 point 9 2 - correct. response - correct Answer Explanation The predicted number of minutes spent reading is 1$$1 - no response given. Correct answers: Substitute 67 for x into the line of best fit to estimate the number of minutes spent reading for someone who spent 67 minutes studying: yˆ=0.16(67)+36.2=46.92. Question The table shows data collected on the relationship between the time spent studying per day and the time spent reading per day. The line of best fit for the data is yˆ=0.16x+36.2. Studying (Minutes) Reading (Minutes) (a) According to the line of best fit, the predicted number of minutes spent reading for someone who spent 67 minutes studying is 46.92. (b) Is it reasonable to use this line of best fit to make the above prediction? The estimate, a predicted time of 46.92 minutes, is both reliable and reasonable. The estimate, a predicted time of 46.92 minutes, is both unreliable and unreasonable. The estimate, a predicted time of 46.92 minutes, is reliable but unreasonable. The estimate, a predicted time of 46.92 minutes, is unreliable but reasonable. Answer Explanation Correct answer: The data in the table only includes studying times between 50 and 110 minutes, so the line of best fit gives reliable and reasonable predictions for values of x between 50 and 110. Since 67 is between these values, the estimate is both reliable and reasonable. Question Michelle is studying the relationship between the hours worked (per week) and time spent reading (per day) and has collected the data shown in the table. The line of best fit for the data is yˆ=−0.79x+98.8. Assume the line of best fit is significant and there is a strong linear relationship between the variables. Hours Worked (per week) Minutes Reading (per day) (a) According to the line of best fit, what would be the predicted number of minutes spent reading for a person who works 27 hours (per week)? Round your answer to two decimal places, as needed. The predicted number of minutes spent reading is 77 point 4 7$$77.4777 point 4 7 - correct. response - correct Answer Explanation The predicted number of minutes spent reading is 1$$1 - no response given. Correct answers: Substitute 27 for x into the line of best fit to estimate the number of minutes spent reading for a person who works 27 hours (per week): yˆ=−0.79(27)+98.8=77.47. Question Michelle is studying the relationship between the hours worked (per week) and time spent reading (per day) and has collected the data shown in the table. The line of best fit for the data is yˆ=−0.79x+98.8. Hours Worked (per week) Minutes Reading (per day) (a) According to the line of best fit, the predicted number of minutes spent reading for a person who works 27 hours (per week) is 77.47. (b) Is it reasonable to use this line of best fit to make the above prediction? The estimate, a predicted time of 77.47 minutes, is unreliable but reasonable. The estimate, a predicted time of 77.47 minutes, is reliable but unreasonable. The estimate, a predicted time of 77.47 minutes, is both unreliable and unreasonable. The estimate, a predicted time of 77.47 minutes, is both reliable and reasonable. Answer Explanation Correct answer: The data in the table only includes the time worked between 30 and 60 hours, so the line of best fit gives reliable and reasonable predictions for values of x between 30 and 60. Since 27 is not between these values, the estimate is not reliable. However, 77.47 minutes is a reasonable time. Question A medical experiment on tumor growth gives the following data table. x y The least squares regression line was found. Using technology, it was determined that the total sum of squares (SST ) was 3922.8 and the sum of squares of regression (SSR ) was 3789.0 . Calculate R2 , rounded to three decimal places. 0 point 9 6 6$$0.9660 point 9 6 6 - correct Correct answers: Answer Explanation $$no response given Correct answers: • 00 point 9 6 6$0.966$0.966 R2=SSRSST R2=3789.03922.8 R2=0.966 • Something's not right... There is an error in the instruction or question. • This looks broken... A graph/image/equation/video isn't working • I cannot enter my answer. A problem is preventing me from entering an answer to this question. • I have an idea! I have some feedback/suggestions. Question A scientific study on mesothelioma caused by asbestos gives the following data table. Micrograms of asbestos inhaled Area of scar tissue (cm2) Using technology, it was determined that the total sum of squares (SST) was 1421.2 and the sum of squares due to error (SSE) was 903.51 . Calculate R2 and determine its meaning. Round your answer to four decimal places. R2=0.3643 Therefore, 36.43% of the variation in the observed y -values can be explained by the estimated regression equation. R2=0.3643 Therefore, 0.3643% of the variation in the observed y -values can be explained by the estimated regression equation. R2=0.6357 Therefore, 63.57% of the variation in the observed y -values can be explained by the estimated regression equation. R2=0.6357 Therefore, 0.6357% of the variation in observed y -values can be explained by the estimated regression equation. Answer Explanation Correct answer: Question R2=1−SSESST R2=1−903..2 R2=1−0.6357 R2=0.3643 R2=36.43% A new mine opened and the number of dump truck loads of material removed was recorded. The table below shows the number of dump truck loads of material removed and the number of days since the mine opened. Days (since opening) # of dump truck loads 2 45 5 53 8 A least squares regression line was found. Using technology, it was determined that the total sum of squares (SST ) was 278.0 and the sum of squares of regression (SSR ) was 274.3 . Use these values to calculate the coefficient of determination. Round your answer to three decimal places. 0.987 0.013 0.993 Answer Explanation Correct answer: 0.987 R2=SSRSST R2=274.3278.0 R2=0.987 Your answer: 0.993 The coefficient of determination is SSRSST and not the square root of that value. • Something's not right... There is an error in the instruction or question. • This looks broken... A graph/image/equation/video isn't working • I cannot enter my answer. A problem is preventing me from entering an answer to this question. • I have an idea! I have some feedback/suggestions. Question A new mine opened and the number of dump truck loads of material removed was recorded. The table below shows the number of dump truck loads of material removed and the number of days since the mine opened. Days (since opening) # of dump truck loads A least squares regression line was found. Using technology, it was determined that the total sum of squares (SST) was 2349 and the sum of squares of error (SSE) was 329. Use these values to calculate the coefficient of determination. Round your answer to three decimal places. 0.860 0.140 2020.000 Answer Explanation Correct answer: 0.860 R2=1−SSESST R2=1− R2=1−0.140 R2=0.860 Question A scientific study on bat populations gives the following data table. x y x y 21 18 26 25 31 21 A least squares regression line was found. Using technology, it was determined that the total sum of squares (SST ) was 46.8 and the sum of squares of regression (SSR ) was 14.55 . Use these values to calculate the percent of the variability in y that can be explained by variability in the regression model. Round your answer to the nearest integer. 31% 69% 0.69% 13% Answer Explanation Correct answer: 31% R2=SSRSST R2=14.5546.8 R2=0.31 R2=31% Your answer: 69% The coefficient of determination is SSRSST and not 1−SSRSST . • Something's not right... There is an error in the instruction or question. • This looks broken... A graph/image/equation/video isn't working • I cannot enter my answer. A problem is preventing me from entering an answer to this question. • I have an idea! I have some feedback/suggestions. Question A fishing enthusiast puts out different numbers of lines at once on several fishing trips to the same location and records the number of fish he catches on each trip. The table below shows the number of lines and number of fish caught on his trips. Fishing lines Fish caught 4 13 5 15 7 Using technology, it was determined that the total sum of squares (SST) was 203.20 and the sum of squares of error (SSE) was 41.62 . Use these values to calculate the coefficient of determination. 0.7952 0.2049 161.5 0.3825 Answer Explanation Correct answer: 0.7952 R2=1−SSESST R2=1−41.62203.20 R2=1−0.2048 R2=0.7952 Your answer: 161.5 The coefficient of determination is 1−SSESST and not SSE−SST . Also the coefficient of determination must be between 0 and 1 . Question A scientific study on calorie intake gives the following data table. Calorie intake (1000) Weight gained (Ounces) Calorie intake (1000) Weight gained (Ounces) 13 Using technology, it was determined that the total sum of squares (SST) was 237.2 , the sum of squares regression (SSR) was 177.96 , and the sum of squares due to error (SSE) was 59.244 . Calculate R2 and determine its meaning. Round your answer to four decimal places. R2=0.2498 Therefore, 24.98% of the variation in the observed y -values can be explained by the estimated regression equation. R2=0.7503 Therefore, 75.03% of the variation in the observed y -values can be explained by the estimated regression equation. R2=1.3329 Therefore, 13.329% of the variation in the observed y -values can be explained by the estimated regression equation. R2=0.3329 Therefore, 33.29% of the variation in the observed y -values can be explained by the estimated regression equation. Answer Explanation Correct answer: Your answer: R2=SSRSST R2=177.96237.2 R2=0.7503 R2=75.03% The coefficient of determination is SSRSST and not SSESST Question A scientific study on graphite density gives the following data table. Distance from center of vein Densit y Using technology, it was determined that the total sum of squares (SST) was 542.07 , the sum of squares regression (SSR) was 521.02 , and the sum of squares due to error (SSE) was 21.044 . Calculate R2 and determine its meaning. Round your answer to four decimal places. R2=0.0388 Therefore, 3.88% of the variation in the observed y -values can be explained by the estimated regression equation. R2=0.0404 Therefore, 4.04% of the variation in the observed y -values can be explained by the estimated regression equation. R2=0.9612 Therefore, 96.12% of the variation in the observed y -values can be explained by the estimated regression equation. R2=1.0404 Therefore, 4.04% of the variation in the observed y -values can be explained by the estimated regression equation. Answer Explanation Correct answer: R2=SSRSST R2=521.02542.07 R2=0.9612 R2=96.12% • Something's not right... There is an error in the instruction or question. • This looks broken... A graph/image/equation/video isn't working • I cannot enter my answer. A problem is preventing me from entering an answer to this question. • I have an idea! I have some feedback/suggestions. Question Given the SSE, SSR, and SST, find the variance in the dependent variable that can't be explained by the variance in the independent variable. SSE 15 SSR 25 SST 40 Answer 1: Answer 2: Answer Explanation Correct answers: The sum of squares due to error, SSE, is 15, which is equal to the variance in the dependent variable, y, that can't be explained by the variance in the independent variable, x. Given the SSR and SSE, find SST. SSR 48 SSE 28 Answer 1: Answer 2: Answer Explanation Correct answers: Question SST=SSR+SSE SST=48+28 SST=76 For a particular regression equation, SSR=325 and SST=550. What is SSE? Answer 1: Answer 2: Answer Explanation Correct answers: Substitute the given values for SST and SSR into the relationship SST=SSR+SSE and solve for SSE. SST−325SSE=SSR+SSE=325+SSE=SSE=225 Question For a particular regression equation, SSE=19 and SST=31. What is SSR? Answer Explanation Correct answers: Substitute the given values for SST and SSR into the relationship SST=SSR+SSE and solve for SSE. SST3131−19SSR=SSR+SSE=SSR+19=SSR=12 Question Given the SSR and SSE, find SST. SS 2 R 7 SS 1 E 0 Answer Explanation Correct answers: Question SST=SSR+SSE SST=27+10 SST=37 A scientific study on construction delays gives the following data table. Construction delay (hours) Increased cost ($1000) Using technology, it was determined that the total sum of squares (SST) was 2542.8 , the sum of squares regression (SSR) was 2194.8 , and the sum of squares due to error (SSE) was 347.99 . Calculate R2 and determine its meaning. Round your answer to four decimal places. R2=0.8631 Therefore, 86.31% of the variation in the observed y -values can be explained by the estimated regression equation. R2=1.1586 Therefore, 1.1586% of the variation in the observed y -values can be explained by the estimated regression equation. R2=0.1369 Therefore, 13.69% of the variation in the observed y -values can be explained by the estimated regression equation. R2=0.1586 Therefore, 15.86% of the variation in the observed y -values can be explained by the estimated regression equation. Answer Explanation Correct answer: R2=SSRSST R2=2194.82542.8 R2=0.8631 R2=86.31% Question Given the SSE, SSR, and SST, find the variance in the dependent variable that can be explained by the variance in the independent variable. SSE 2 SSR 6 SST 8 Answer Explanation Correct answers: The sum of squares of regression, SSR, is 6, which is equal to the variance in the dependent variable, y, that can be explained by the variance in the independent variable, x. Question Given the SSE, SSR, and SST, find the variance in the dependent variable that can be explained by the variance in the independent variable. SSE 12 SSR 24 SST 36 Answer Explanation Correct answers: The sum of squares of regression, SSR, is 24, which is equal to the variance in the dependent variable, y, that can be explained by the variance in the independent variable, x Question A scientific study on calorie intake gives the following data table. Calorie intake (1000) Hours of exercise need to maintain weight Using technology, it was determined that the total sum of squares (SST) was 76 , the sum of squares regression (SSR) was 54.850 , and the sum of squares due to error (SSE) was 21.150 . Calculate R2 and determine its meaning. Round your answer to four decimal places? R2=0.3856 Therefore, 38.56% of the variation in the observed y -values can be explained by the estimated regression equation. R2=0.7217 Therefore, 72.17% of the variation in the observed y -values can be explained by the estimated regression equation. R2=1.3856 Therefore, 13.856% of the variation in the observed y -values can be explained by the estimated regression equation. R2=0.2783 Therefore, 27.83% of the variation in the observed y -values can be explained by the estimated regression equation. Answer Explanation Correct answer: Question R2=SSRSST R2=54.86176.012 R2=0.7217 R2=72.17% A scientific study on lift strength gives the following data table. Lift strength (Tons) Time to move load (seconds) Using technology, it was determined that the total sum of squares (SST) was 1989.2 , the sum of squares regression (SSR) was 1598.1 , and the sum of squares due to error (SSE) was 391.10 . Calculate R2 and determine its meaning. Round your answer to four decimal places. R2=0.2447 Therefore, 24.47% of the variation in the observed y -values can be explained by the estimated regression equation. R2=0.1966 Therefore, 19.66% of the variation in the observed y -values can be explained by the estimated regression equation. R2=0.8034 Therefore, 80.34% of the variation in the observed y -values can be explained by the estimated regression equation. R2=1.2447 Therefore, 12.447% of the variation in the observed y -values can be explained by the estimated regression equation. Answer Explanation Correct answer: R2=SSRSST R2=1598.11989.2 R2=0.8034 R2=80.34% Show Less