3. Given are five observations collected in a regression study on two variables.Xi1326920Yi7 18 918926 23a. Develop a scatter diagram for these data.b. Develop the estimated regression equation for these data.C.Use the estimated regression equation to predict the value of y when x = 6.

Solution-: Given data: X: 2,6,9,13,20 Y: 7,18,9,26,23 Our aim to find (a) Draw the scatter diagram…

Answered: 3. Given are five observations…

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 4GP

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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?
Does Table 1 represent a linear function? If so, finda linear equation that models the data.
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
1. Given the data below, compute the following: a. Correlation coefficient i. r= ii. Is the relationship (select one) . Strongly correlated Moderately correlated • Weakly correlated • No correlation iii. In what direction is the correlation, interpret in the context of the data. Calculate the Slope C. Calculate the Intercept d. Use parts b. and c. to write down the regression equation. Hours x Test Scores n Hours Score Hours^2 Score ^2 Studying 0 40 2 51 4 64 5 69 73 5 75 6 93 7 90 9 7 95 SUMS e. Use the regression equation to predict the test scores (y's) for the given hours of stu i. X=3 hours ii. X=6.5 hours b. 1 2 3 4 5 6 7 8 5
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. (b) x = 641 feet (d) x = 725 feet (a) x = 500 feet (c) x = 345 feet 483 492 510 518 621 Height, x Stories, y 758 37 35 41 44 47 51 Find the regression equation. ŷ=x+ (O Q. (Round the slope to three decimal places as needed. Round the y-intercept to two decimal places as needed.) th Choose the correct graph below. 12 OD. OC. O B. OA. 60- 60- 60- 60- cion 0- ons 800 800 800 800 Height (feet) Height (feet) Height (feet) Height (feet) Quest (a) Predict the value of y for x = 500. Choose the correct answer below. ed Que O A. 51 d Quest O B. 39 O C. 47 Click to select your answer(s). tv DEC MacBook…
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. (a) x = 499 feet (c) x = 798 feet (b) x = 652 feet (d) x = 736 feet 619 519 508 491 474 Height, x Stories, y 775 53 47 44 43 39 37 Find the regression equation. (Round the slope to three decimal places as needed. Round the y-intercept to two decimal places as needed.)
Is there a relationship between wine consumption and deaths from heart disease? The table gives data from 19 developed countries. a. Use the statistical menu of your graphing utility to enter the 19 ordered pairs of data items shown in the table.b. Use the scatter plot capability to draw a scatter plot of the data. c. Select the linear regression option. Use your utility to obtain values for a and b for the equation of the regression line, y = ax + b. d.Use the appropriate sequence to graph the regression equation on top of the points in the scatter plot.
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. (a) x = 503 feet (c) x = 318 feet (b) x= 649 feet (d) x D724 feet 766 508 494 484 Height, x Stories, y 620 520 51 46 '44 43 39 38 (a) Predict the value of y for x= 503. Choose the correct answer below. O A. 47 O B. 41 O C. 50 O D. not meaningful (b) Predict the value of y for x = 649. Choose the correct answer below. O A. 34 O B. 41 n C. 47
The accompanying data are the number of wins and the eamed run averages (mean number of earned runs allowed per nine innings pitched) for eight baseball pitchers in a recent season. Find the equation of the regression line. Then construct a scatter plot of the data and draw the regression line. Then use the regression equation to predict the value of y for each of the given x-values, if meaningful. If the x-value is not meaningful to predict the value of y, explain why not. (a) x=5 wins (b) x= 10 wins (c) x= 19 wins (d) x= 15 wins E Click the icon to view the table of numbers of wins and eamed run average. The equation of the regression line is y-x+O (Round to two decimal places as needed.) Construct a scatter plot of the data and draw the regression line. Choose the correct graph below. OA. OB. В. Oc. D. AERA AERA AERA AERA 4- 4- 4- 4- 2 2- 2- 2- 12 18 24 6 12 18 24 12 18 24 12 18 24 Wina Wins Wina Wins (a) Predict the ERA for 5 wins, if it is meaningful. Select the correct choice…
Four pairs of data yield an r = .902 and a regression equation of y = 4x + 7. Also the average y-value is 12.45. What is the best predicted value for y if x = 3.8 ? X 2 3 5 6 Y 4 9 14 20
Listed below are systolic blood pressure measurements (in mm Hg) obtained from the same woman. Find the regression equation, letting the right arm blood pressure be the predictor (x) variable. Find the best predicted systolic blood pressure in the left arm given that the systolic blood pressure in the right arm is 80 mm Hg. Use a significance level of 0.05. Right Arm 100 99 91 78 77 D Left Arm 177 171 144 146 146 Data Table Click the icon to view the critical values of the Pearson correlation coefficient r The regression equation is y= Round to one decimal place as needed.) NOTE: To test Ho p= 0 against H,: p±0, reject Ho if the absolute value of r is greater than the critical value in the table. X. a = 0.05 a = 0.01 4. 0.950 0.990 0.878 0.959 0.811 0.917 0.754 0.875 0.834 7 0.707 9. 0.666 0.798 10 0.632 0.765 11 0.602 0.735 12 0.576 0.708 13 0.553 0.684 14 0.532 0.661 15 0.514 0.641 16 0.497 0.623 17 0.482 0.606 18 0.468 0.590 0.456 0.575 0.561 19 20 0.444 25 0.396 0.505 30 0.361…
13) Use computer software to find the multiple regression equation. Can the equation be used for prediction? An anti-smoking group used data in the table to relate the carbon monoxide( CO) of various brands of cigarettes to their tar and nicotine (NIC) content. 13). CO TAR NIC 15 1.2 16 15 1.2 16 17 1.0 16 6. 0.8 1 0.1 1 8. 0.8 8. 10 0.8 10 17 1.0 16 15 1.2 15 11 0.7 9. 18 1.4 18 16 1.0 15 10 0.8 9. 0.5 18 1.1 16 A) CO = 1.37 + 5.50TAR – 1.38NIC; Yes, because the P-value is high. B) CÓ = 1.37 - 5.53TAR + 1.33NIC; Yes, because the R2 is high. C) CO = 1.25 + 1.55TAR – 5.79NIC; Yes, because the P-value is too low. D) CO = 1.3 + 5.5TAR - 1.3NIC; Yes, because the adjusted R2 is high. %3D

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3. Given are five observations collected in a regression study on two variables. 2 6 9 13 20 Yi 7 18 9 26 23 a. Develop a scatter diagram for these data. b. Develop the estimated regression equation for these data. C. Use the estimated regression equation to predict the value of y when x = 6.