Elementary Statistics
12th Edition
ISBN: 9780321836960
Author: Mario F. Triola
Publisher: PEARSON
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Chapter 10, Problem 3CQQ
To determine
To obtain: The best predicted diastolic reading, given systolic reading of 125.
To explain: The method to find the best predicted value.
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The table below gives the age and bone density for five randomly selected women. Using this data, consider the equation of the regression line, bo + b₁x, for
predicting a woman's bone density based on her age. 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.
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A football coach is looking for a way to identify players that are "under weight". The coach decides to get
data for each player's height (x, in inches) and weight (y, in pounds), then does a linear regression. The
results are:
y = - 52 + 3x,r = 0.9 and the standard error is Se = 10.4.
Since there is a strong linear correlation the coach, who also majored in Statistics, decides to identify all
"outliers" in the data.
Obviously, any player whose weight is above the regression line is not "under weight". So the only outliers
the coach is interested in are those that are below the regression line.
What is the lowest weight possible for a 69 inch player to not be considered "under weight"? Do not round.
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A football coach is looking for a way to identify players that are "under weight". The coach decides to get
data for each player's height (x, in inches) and weight (y, in pounds), then does a linear regression. The
results are:
y = - 57 + 3.2a, r = 0.87 and the standard error is S. = 11.4.
Since there is a strong linear correlation the coach, who also majored in Statistics, decides to identify all
"outliers" in the data.
Obviously, any player whose weight is above the regression line is not "under weight". So the only outliers
the coach is interested in are those that are below the regression line.
What is the lowest weight possible for a 71 inch player to not be considered "under weight"? Do not round.
Chapter 10 Solutions
Elementary Statistics
Ch. 10.2 - Notation For each of several randomly selected...Ch. 10.2 - Physics Experiment A physics experiment consists...Ch. 10.2 - Cause of High Blood Pressure Some studies have...Ch. 10.2 - Notation What is the difference between the...Ch. 10.2 - Interpreting r. In Exercises 5-8, use a...Ch. 10.2 - Interpreting r. In Exercises 5-8, use a...Ch. 10.2 - Interpreting r. In Exercises 5-8, use a...Ch. 10.2 - Cereal Killers The amounts of sugar (grams of...Ch. 10.2 - Explore! Exercises 9 and 10 provide two data sets...Ch. 10.2 - Explore! Exercises 9 and 10 provide two data sets...
Ch. 10.2 - Outlier Refer in the accompanying...Ch. 10.2 - Clusters Refer to the following Minitab-generated...Ch. 10.2 - Testing for a Linear Correlation. In Exercises...Ch. 10.2 - Prob. 14BSCCh. 10.2 - Testing for a Linear Correlation. In Exercises...Ch. 10.2 - Testing for a Linear Correlation. In Exercises...Ch. 10.2 - Testing for a Linear Correlation. In Exercises...Ch. 10.2 - Testing for a Linear Correlation. In Exercises...Ch. 10.2 - Prob. 19BSCCh. 10.2 - Prob. 20BSCCh. 10.2 - Testing for a Linear Correlation. In Exercises...Ch. 10.2 - Testing for a Linear Correlation. In Exercises...Ch. 10.2 - Prob. 23BSCCh. 10.2 - Prob. 24BSCCh. 10.2 - Testing for a Linear Correlation. In Exercises...Ch. 10.2 - Prob. 26BSCCh. 10.2 - Testing for a Linear Correlation. In Exercises...Ch. 10.2 - Testing for a Linear Correlation. In Exercises...Ch. 10.2 - Large Data Sets. In Exercises 29-32, use the data...Ch. 10.2 - Large Data Sets. In Exercises 29-32, use the data...Ch. 10.2 - Appendix B Data Sets. In Exercises 29-34, use the...Ch. 10.2 - Large Data Sets. In Exercises 29-32, use the data...Ch. 10.2 - Transformed Data In addition to testing for a...Ch. 10.2 - Prob. 34BBCh. 10.3 - Notation and Terminology If we use the paired...Ch. 10.3 - Best-Fit Line In what sense is the regression line...Ch. 10.3 - Prob. 3BSCCh. 10.3 - Notation What is the difference between the...Ch. 10.3 - Making Predictions. In Exercises 5-8, let the...Ch. 10.3 - Making Predictions. In Exercises 5-8, let the...Ch. 10.3 - Making Predictions. In Exercises 5-8, let the...Ch. 10.3 - Making Predictions. In Exercises 5-8, let the...Ch. 10.3 - Finding the Equation of the Regression Line. In...Ch. 10.3 - Finding the Equation of the Regression Line. In...Ch. 10.3 - Effects of an Outlier Refer to the Mini...Ch. 10.3 - Effects of Clusters Refer to the Minitab-generated...Ch. 10.3 - Regression and Predictions. Exercises 13-28 use...Ch. 10.3 - Regression and Predictions. Exercises 13-28 use...Ch. 10.3 - Regression and Predictions. Exercises 13-28 use...Ch. 10.3 - Regression and Predictions. Exercises 13-28 use...Ch. 10.3 - Regression and Predictions. Exercises 13-28 use...Ch. 10.3 - Regression and Predictions. Exercises 13-28 use...Ch. 10.3 - Regression and Predictions. Exercises 13-28 use...Ch. 10.3 - Regression and Predictions. Exercises 13-28 use...Ch. 10.3 - Regression and Predictions. Exercises 13-28 use...Ch. 10.3 - Regression and Predictions. Exercises 13-28 use...Ch. 10.3 - Regression and Predictions. Exercises 13-28 use...Ch. 10.3 - Regression and Predictions. Exercises 13-28 use...Ch. 10.3 - Regression and Predictions. Exercises 13-28 use...Ch. 10.3 - Regression and Predictions. Exercises 1328 use the...Ch. 10.3 - Regression and Predictions. Exercises 1328 use the...Ch. 10.3 - Regression and Predictions. Exercises 1328 use the...Ch. 10.3 - Large Data Sets. Exercises 2932 use the same...Ch. 10.3 - Large Data Sets. Exercises 2932 use the same...Ch. 10.3 - Prob. 31BSCCh. 10.3 - Large Data Sets. Exercises 29-32 use the same...Ch. 10.3 - Prob. 33BBCh. 10.3 - Prob. 34BBCh. 10.4 - Prob. 1BSCCh. 10.4 - Prediction Interval Using the heights and weights...Ch. 10.4 - Prob. 3BSCCh. 10.4 - Prob. 4BSCCh. 10.4 - Interpreting the Coefficient of Determination. In...Ch. 10.4 - Interpreting the Coefficient of Determination. In...Ch. 10.4 - Interpreting the Coefficient of Determination. In...Ch. 10.4 - Interpreting the Coefficient of Determination. In...Ch. 10.4 - Prob. 9BSCCh. 10.4 - Prob. 10BSCCh. 10.4 - Prob. 11BSCCh. 10.4 - Prob. 12BSCCh. 10.4 - Prob. 13BSCCh. 10.4 - Prob. 14BSCCh. 10.4 - Prob. 15BSCCh. 10.4 - Prob. 16BSCCh. 10.4 - Variation and Prediction Intervals. In Exercises...Ch. 10.4 - Prob. 18BSCCh. 10.4 - Prob. 19BSCCh. 10.4 - Prob. 20BSCCh. 10.4 - Confidence Intervals for 0 and 1 Confidence...Ch. 10.4 - Confidence Interval for Mean Predicted Value...Ch. 10.5 - Prob. 1BSCCh. 10.5 - Best Multiple Regression Equation For the...Ch. 10.5 - Adjusted Coefficient of Determination For Exercise...Ch. 10.5 - Interpreting R2 For the multiple regression...Ch. 10.5 - Prob. 5BSCCh. 10.5 - Prob. 6BSCCh. 10.5 - Prob. 7BSCCh. 10.5 - Prob. 8BSCCh. 10.5 - Prob. 9BSCCh. 10.5 - Prob. 10BSCCh. 10.5 - Prob. 11BSCCh. 10.5 - City Fuel Consumption: Finding the Best Multiple...Ch. 10.5 - Prob. 13BSCCh. 10.5 - Prob. 14BSCCh. 10.5 - Appendix B Data Sets. In Exercises 13-16, refer to...Ch. 10.5 - Appendix B Data Sets. In Exercises 13-16, refer to...Ch. 10.5 - Prob. 17BBCh. 10.5 - Prob. 18BBCh. 10.5 - Dummy Variable Refer to Data Set 9 Bear...Ch. 10.6 - Prob. 1BSCCh. 10.6 - Prob. 2BSCCh. 10.6 - Super Bowl and R2 Let x represent years coded as...Ch. 10.6 - Prob. 4BSCCh. 10.6 - Prob. 5BSCCh. 10.6 - Finding the Best Model. In Exercises 5-16,...Ch. 10.6 - Prob. 7BSCCh. 10.6 - Prob. 8BSCCh. 10.6 - Finding the Best Model. In Exercises 5-16,...Ch. 10.6 - Finding the Best Model. In Exercises 5-16,...Ch. 10.6 - Prob. 11BSCCh. 10.6 - Prob. 12BSCCh. 10.6 - Prob. 13BSCCh. 10.6 - Prob. 14BSCCh. 10.6 - Prob. 15BSCCh. 10.6 - Prob. 16BSCCh. 10.6 - Prob. 18BBCh. 10 - The exercises arc based on the following sample...Ch. 10 - Prob. 2CQQCh. 10 - Prob. 3CQQCh. 10 - The exercises are based on the following sample...Ch. 10 - The exercises are based on the following sample...Ch. 10 - Prob. 6CQQCh. 10 - Prob. 7CQQCh. 10 - Prob. 8CQQCh. 10 - Prob. 9CQQCh. 10 - Prob. 10CQQCh. 10 - Old Faithful The table below lists measurements...Ch. 10 - Prob. 2RECh. 10 - Prob. 3RECh. 10 - Prob. 4RECh. 10 - Prob. 5RECh. 10 - Prob. 1CRECh. 10 - Prob. 2CRECh. 10 - Prob. 3CRECh. 10 - Prob. 4CRECh. 10 - Effectiveness of Diet. Listed below are weights...Ch. 10 - Prob. 6CRECh. 10 - Prob. 7CRECh. 10 - Effectiveness of Diet. Listed below are weights...Ch. 10 - Prob. 9CRECh. 10 - Prob. 10CRECh. 10 - Critical Thinking: Is replication validation? The...Ch. 10 - Prob. 2FDDCh. 10 - Prob. 3FDD
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- Find the equation of the regression line for the following data set. x 1 2 3 y 0 3 4arrow_forwardFor the following exercises, use Table 4 which shows the percent of unemployed persons 25 years or older who are college graduates in a particular city, by year. Based on the set of data given in Table 5, calculate the regression line using a calculator or other technology tool, and determine the correlation coefficient. Round to three decimal places of accuracyarrow_forwardOlympic 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?arrow_forward
- 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.arrow_forwardListed below are systolic blood pressure measurements (in mm Hg) obtained from the same woman. Find the regression equation, letting the fight 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 90 mm Hg. Use a significance level of 0.05. Right Arm Left Arm % 102 101 94 80 79 177 172 143 143 143 Click the icon to view the critical values of the Pearson correlation coefficient r The regression equation is y=+x. (Round to one decimal place as needed.). Given that the systolic blood pressure in the right arm is 90 mm Hg, the best predicted systolic blood pressure in the left arm is mm Hg. (Round to one decimal place as needed) M H N H & C Copyright ©2022 Pearson Education Inc. All rights reserved. | Terms of Use | Privacy Policy | Permissions | Contact Us | a 33 M 0 8 K Vi 1. fio 11 O More (2) { 87°F Next [ insert prt sc 7:46 8/5/2 backspacearrow_forwardA football coach is looking for a way to identify players that are "under weight". The coach decides to get data for each player's height (x, in inches) and weight (y, in pounds), then does a linear regression. The results are: y = - 58 + 4x,r = 0.95 and the standard error is Se = 13. Since there is a strong linear correlation the coach, who also majored in Statistics, decides to identify all "outliers" in the data. Obviously, any player whose weight is above the regression line is not "under weight". So the only outliers the coach is interested in are those that are below the regression line. What is the lowest weight possible for a 67 inch player to not be considered "under weight"? Do not round. Submit Question MacBook Air >> F2 E3 000 E4 ES F6 F7 F8 F10 F9 24 4. %23 * 3. 8. 9. Y G H. J K C V M 3 BELK commandarrow_forward
- The table below gives the age and bone density for five randomly selected women. Using this data, consider the equation of the regression line,ŷ = bo + b₁x, for predicting a woman's bone density based on her age. 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. esc alt e. Step 4 of 6: Find the estimated value of y when .x = 64. Round your answer to three decimal places. Answer How to enter your answer (opens in new window) hp ! 1 9 a Z F 2 W S X → 3 # e d C C $ 4 r f V Age 36 52 58 64 68 Bone Density 336 335 318 317 314 % 5 t g 6 b y h & 7 n u * 8 m O i N k ( 9 * O alt ) 0 1 CO Р J > - - : ; ctrl Tables { [ ? Previous step answers Keypad Keyboard Shortcuts Submit Answer 4 + = Copy Data 11 1 Table Dec 2 } 5:01 USE YOUR SMARTPHONE FOR Reviews Videos Features A backspace…arrow_forwardA football coach is looking for a way to identify players that are "under weight". The coach decides to get data for each player's height (x, in inches) and weight (y, in pounds), then does a linear regression. The results are: 58+3.7x, r = 0.86 and the standard error is Se = 10.4. Since there is a strong linear correlation the coach, who also majored in Statistics, decides to identify all "outliers" in the data. Obviously, any player whose weight is above the regression line is not "under weight". So the only outliers the coach is interested in are those that are below the regression line. What is the lowest weight possible for a 75 inch player to not be considered "under weight"? Do not round. Submit Question ctor GSearch or type URL & % 24 6 7arrow_forwardThe table below gives the age and bone density for five randomly selected women. Using this data, consider the equation of the regression line, y = bo + b₁x, for predicting a woman's bone density based on her age. 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. Age Answer How to enter your answer (opens in new window) Bone Density 40 61 62 68 69 357 350 343 340 315 Step 6 of 6: Find the value of the coefficient of determination. Round your answer to three decimal places. Tables Copy Data Keypad Keyboard Shortcuts Table Previous step answers Submit Answer Dec 3 4:51 VIarrow_forward
- Please see attached image.arrow_forwardThe table below gives the age and bone density for five randomly selected women. Using this data, consider the equation of the regression line, y = bo + bjx, for predicting a woman's bone density based on her age. 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. Age 35 50 54 61 66 Bone Density 354 353 350 334 332 Tab Copy Data Step 6 of 6: Find the value of the coefficient of determination. Round your answer to three decimal places. 田 Tables 國 Key Answer Keyboard Sho How to enter your answer (opens in new window) Previous step ar Submit An © 2022 Hawkes Learning tv APR 24 MacBook Proarrow_forwardSuppose the athletic director at a university would like to develop a regression model to predict the point differential for games played by the men's basketball team. A point differential is the difference between the final points scored by two competing teams. A positive differential is a win, and a negative differential is a loss. For a random sample of games, the point differential was calculated, along with the number of assists, rebounds, turnovers, and personal fouls. Use the data in the accompanying table attached below to complete parts a through e below. Assume a = 0.05. a) Using technology, construct a regression model using all three independent variables. y = __ + (_)x1 + (_)x2 + (_)x3 + (_)x4 b) Test the significance of each independent variable using a= 0.10. c) interpret the p-value for each independent variable. d) Construxt a 90% confidence interval for the regression coefficients for each independent variable and interpret the meaning. e) Using the results from…arrow_forward
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