Find the equation of the regression line for the given data. Then construct a scatter plot of the data and draw the regression line. (The pair of variables have a significant correlation.) Then use the regression equation to predict the value of y for each of the given x-values, if meaningful. The table below shows the heights (in feet) and the number of stories of six notable buildings in a city. Height, x Stories, y 520 44 508 766 51 620 46 494 37 484 36 (a) x = 500 feet (c) x = 310 feet (b) x= 642 feet (d) x= 731 feet 41 Find the regression equation. (Round the slope to three decimal places as needed. Round the y-intercept to two decimal places as needed.)

Find the equation of the regression line for the given data. Then construct a scatter plot of the data and draw the regression line. (The pair of variables have a significant correlation.) Then use the regression equation to predict the value of y for each of the given x-values, if meaningful. The table below shows the heights (in feet) and the number of stories of six notable buildings in a city. Height, x Stories, y 520 44 508 766 51 620 46 494 37 484 36 (a) x = 500 feet (c) x = 310 feet (b) x= 642 feet (d) x= 731 feet 41 Find the regression equation. (Round the slope to three decimal places as needed. Round the y-intercept to two decimal places as needed.)

Glencoe Algebra 1, Student Edition, 9780079039897, 0079039898, 2018

18th Edition

ISBN:9780079039897

Author:Carter

Publisher:Carter

Chapter4: Equations Of Linear Functions

Section4.6: Regression And Median-fit Lines

Problem 6PPS

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Olympic Pole Vault The graph in Figure 7 indicates that in recent years the winning Olympic men’s pole vault height has fallen below the value predicted by the regression line in Example 2. This might have occurred because when the pole vault was a new event there was much room for improvement in vaulters’ performances, whereas now even the best training can produce only incremental advances. Let’s see whether concentrating on more recent results gives a better predictor of future records. (a) Use the data in Table 2 (page 176) to complete the table of winning pole vault heights shown in the margin. (Note that we are using x=0 to correspond to the year 1972, where this restricted data set begins.) (b) Find the regression line for the data in part ‚(a). (c) Plot the data and the regression line on the same axes. Does the regression line seem to provide a good model for the data? (d) What does the regression line predict as the winning pole vault height for the 2012 Olympics? Compare this predicted value to the actual 2012 winning height of 5.97 m, as described on page 177. Has this new regression line provided a better prediction than the line in Example 2?
Life Expectancy The following table shows the average life expectancy, in years, of a child born in the given year42 Life expectancy 2005 77.6 2007 78.1 2009 78.5 2011 78.7 2013 78.8 a. Find the equation of the regression line, and explain the meaning of its slope. b. Plot the data points and the regression line. c. Explain in practical terms the meaning of the slope of the regression line. d. Based on the trend of the regression line, what do you predict as the life expectancy of a child born in 2019? e. Based on the trend of the regression line, what do you predict as the life expectancy of a child born in 1580?2300
The following fictitious table shows kryptonite price, in dollar per gram, t years after 2006. t= Years since 2006 0 1 2 3 4 5 6 7 8 9 10 K= Price 56 51 50 55 58 52 45 43 44 48 51 Make a quartic model of these data. Round the regression parameters to two decimal places.
Find the equation of the regression line for the given data. Then construct a scatter plot of the data and draw the regression line. (Each pair of variables has a significant correlation.) Then use the regression equation to predict the value of y for each of the given x-values, if meaningful. The caloric content and the sodium content (in milligrams) for 6 beef hot dogs are shown in the table below. Calories, x Sodium, y150 410180 460120 320120 36070 290190 540 (a) x=160 calories (b) x=90 calories (c) x=130 calories (d) x=220 calories Y=_____x+(___)
Find the equation of the regression line for the given data. Then construct a scatter plot of the data and draw the regression line. (The pair of variables have a significant correlation.) Then use the regression equation to predict the value of y for each of the given x-values, if meaningful. The table below shows the heights (in feet) and the number of stories of six notable buildings in a city. Height, x Stories, y778 51621 47519 46510 42494 39473 38 (a) x=499 feet (b) x=641 feet (c) x=321 feet (d) x=725 fee y=____x+____
Find the equation of the regression line for the given data. Then construct a scatter plot of the data and draw the regression line. (The pair of variables have a significant correlation.) Then use the regression equation to predict the value of y for each of the given x-values, if meaningful. The table below shows the heights (in feet) and the number of stories of six notable buildings in a city. Height, x Stories, y778 51621 47519 45510 42494 39473 38 (a) x=498 feet (b) x=647 feet (c) x=321 feet (d) x=727 fee Find the regression equation.
The prelim grades (x) and midterm grades (y) of a sample of 10 MMW students is modeled by the regression line y = 12.0623 + 0.7771x. Estimate the prelim grade if the midterm grade is 83.
Find the equation of the regression line for the given data. Then construct a scatter plot of the data and draw the regression line. (Each pair of variables has a significant correlation.) Then use the regression equation to predict the value of y for each of the given x-values, if meaningful. The caloric content and the sodium content (in milligrams) for 6 beef hot dogs are shown in the table below. Calories, x 160 180 130 130 90 190 (a) x=150 calories (b) x=100 calories Sodium, y 430 480 320 360 270 530 (c) x=120 calories (d) x=60 calories Find the regression equation. y= x+( ) (Round to three decimal places as needed.) draw a graph predict the value of y for x=150. Choose the correct answer below. A. 172.514 B. 328.874 C. 407.054 D. not meaningful (b) Predict the value of y for x=100. Choose the correct answer below. A. 328.874 B. 172.514…
Find the equation of the regression line for the given data. Then construct a scatter plot of the data and draw the regression line. (Each pair of variables has a significant correlation.) Then use the regression equation to predict the value of y for each of the given x-values, if meaningful. The caloric content and the sodium content (in milligrams) for 6 beef hot dogs are shown in the table below. Calories, x 150 180 130 120 90 190 (a) x=160 calories (b) x=100 calories Sodium, y 430 480 320 360 290 550 (c) x=140 calories (d) x=50 calories Find the regression equation. y=nothingx+(nothing) (Round to three decimal places as needed.) Choose the correct graph below. A. 02000560CaloriesSodium (mg) A scatterplot has a horizontal axis labeled "Calories" from 0 to 200 in increments of 20 and a vertical axis labeled "Sodium (in milligrams)" from 0 to 560 in increments of…
Find the equation of the regression line for the given data. Then construct a scatter plot of the data and draw the regression line. (Each pair of variables has a significant correlation.) Then use the regression equation to predict the value of y for each of the given x-values, if meaningful. The caloric content and the sodium content (in milligrams) for 6 beef hot dogs are shown in the table below. Calories, x 160 170 130 130 90 180 (a) x=150 calories (b) x=100 calories Sodium, y 410 460 350 370 270 510 (c) x=140 calories (d) x=210 calories Find the regression equation. (a) Predict the value of y for x=150 (b) Predict the value of y for x=100. (c) Predict the value of y for x=140. (d) Predict the value of y for x=210.
Find the equation of the regression line for the given data. Then construct a scatter plot of the data and draw the regression line. (Each pair of variables has a significant correlation.) Then use the regression equation to predict the value of y for each of the given x-values, if meaningful. The caloric content and the sodium content (in milligrams) for 6 beef hot dogs are shown in the table below. Calories, X 160 180 120 120 70 190 Sodium, Y 430 480 320 370 240 530 (a) x= 170 calories (b) x= 90 calories (c) x=140 calories (d) x=210 calories
Find the equation of the regression line for the given data. Then construct a scatter plot of the data and draw the regression line. (The pair of variables have a significant correlation.) Then use the regression equation to predict the value of y for each of the given x-values, if meaningful. The number of hours 6 students spent for a test and their scores on that test are shown below. Hours spent studying, x Test score, y0 401 433 523 485 62 6 72 (a) x=2 hours (b) x=2.5 hours (c) x=12 hours (d) x=1.5 hours y=____x + (___)