Statistics: The Art and Science of Learning from Data (4th Edition)
4th Edition
ISBN: 9780321997838
Author: Alan Agresti, Christine A. Franklin, Bernhard Klingenberg
Publisher: PEARSON
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Chapter 12.1, Problem 8PB
a.
To determine
Create a
b.
To determine
Find the prediction equation.
Interpret the slope and y-intercept.
c.
To determine
Find the predicted GPA and residual for Student 1.
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The table below shows the number of state-registered automatic weapons and the murder rate for several
Northwestern states.
X 11.3
y
13.3 10.9 9.9
8.4 6.8 3.9
2.8 2.4 2.5
7 6.7 6.4 6.3
x = thousands of automatic weapons
y = murders per 100,000 residents
This data can be modeled by the equation ŷ = 0.79x + 4.32. Use this equation to answer the following.
Answer = 12.829
0.9
5
A) How many murders per 100,000 residents can be expected in a state with 10.1 thousand automatic
weapons?
Answer = 6.123
x Round to 3 decimal places.
B) How many murders per 100,000 residents can be expected in a state with 2.7 thousand automatic
weapons?
x Round to 3 decimal places.
a) Determine a linear regression equation for the data. Round the slope and y-intercept to the nearest whole number.
b) Find the correlation coefficient, r-value, of the line. Round your answer to the nearest thousandth.
c) Predict the distance the driver would have traveled after 11 hours. Round your answer to the nearest whole number.
The data show the number of viewers for television stars with certain salaries. Find the regression equation, letting salary be the independent (x) variable. Find the best
predicted number of viewers for a television star with a salary of $6 million. Is the result close to the actual number of viewers, 2.9 million? Use a significance level of
0.05
Chapter 12 Solutions
Statistics: The Art and Science of Learning from Data (4th Edition)
Ch. 12.1 - Car mileage and weight The Car Weight and Mileage...Ch. 12.1 - Prob. 2PBCh. 12.1 - Predicting maximum bench strength in males For the...Ch. 12.1 - Prob. 4PBCh. 12.1 - Mu, not y For a population regression equation,...Ch. 12.1 - Prob. 6PBCh. 12.1 - Study time and college GPA Exercise 3.39 in...Ch. 12.1 - Prob. 8PBCh. 12.1 - Cell phone specs Refer to the cell phone data set...Ch. 12.1 - Prob. 10PB
Ch. 12.2 - t-score? A regression analysis is conducted with...Ch. 12.2 - Prob. 12PBCh. 12.2 - Confidence interval for slope Refer to the...Ch. 12.2 - Prob. 14PBCh. 12.2 - Strength through leg press The high school female...Ch. 12.2 - Prob. 16PBCh. 12.2 - More girls are good? Repeat the previous exercise...Ch. 12.2 - CI and two-sided tests correspond Refer to the...Ch. 12.2 - Advertising and sales Each month, the owner of Caf...Ch. 12.2 - Prob. 20PBCh. 12.2 - GPA and skipping classrevisited Refer to the...Ch. 12.2 - Prob. 22PBCh. 12.3 - Dollars and thousands of dollars If a slope is...Ch. 12.3 - Prob. 24PBCh. 12.3 - Sketch scatterplot Sketch a scatterplot,...Ch. 12.3 - Prob. 26PBCh. 12.3 - Body fat For the Male Athlete Strength data file...Ch. 12.3 - Prob. 28PBCh. 12.3 - SAT regression toward mean Refer to the previous...Ch. 12.3 - Prob. 30PBCh. 12.3 - GPA and study time Refer to the association you...Ch. 12.3 - Prob. 32PBCh. 12.3 - Does tutoring help? For a class of 100 students,...Ch. 12.3 - Prob. 34PBCh. 12.3 - Golf regression In the first round of a golf...Ch. 12.3 - Prob. 36PBCh. 12.3 - Food and drink sales The owner of Berthas...Ch. 12.3 - Prob. 38PBCh. 12.3 - Violent crime and single-parent families Use...Ch. 12.4 - Poor predicted strengths The MINITAB output shows...Ch. 12.4 - Prob. 42PBCh. 12.4 - Bench press residuals The figure is a histogram of...Ch. 12.4 - Predicting house prices The House Selling Prices...Ch. 12.4 - Predicting clothes purchases For a random sample...Ch. 12.4 - Prob. 46PBCh. 12.4 - ANOVA table for leg press Exercise 12.15 referred...Ch. 12.4 - Prob. 48PBCh. 12.4 - Variability and F Refer to the previous two...Ch. 12.4 - Understanding an ANOVA table For a random sample...Ch. 12.4 - Predicting cell phone weight Refer to the cell...Ch. 12.4 - Cell phone ANOVA Report the ANOVA table for the...Ch. 12.5 - Savings grow exponentially You invest 100 in a...Ch. 12.5 - Prob. 55PBCh. 12.5 - Prob. 56PBCh. 12.5 - Prob. 57PBCh. 12.5 - Prob. 58PBCh. 12.5 - Prob. 59PBCh. 12.5 - Prob. 60PBCh. 12.5 - Prob. 61PBCh. 12 - Prob. 62CPCh. 12 - Prob. 63CPCh. 12 - Prob. 64CPCh. 12 - Prob. 65CPCh. 12 - Prob. 66CPCh. 12 - Prob. 67CPCh. 12 - Prob. 68CPCh. 12 - Prob. 69CPCh. 12 - Prob. 70CPCh. 12 - Prob. 71CPCh. 12 - Prob. 72CPCh. 12 - Prob. 73CPCh. 12 - Prob. 74CPCh. 12 - World population growth The table shows the world...Ch. 12 - Prob. 76CPCh. 12 - Prob. 77CPCh. 12 - Prob. 78CPCh. 12 - Prob. 79CPCh. 12 - Prob. 81CPCh. 12 - Prob. 82CPCh. 12 - Prob. 83CPCh. 12 - Prob. 84CPCh. 12 - Prob. 85CPCh. 12 - Prob. 86CPCh. 12 - Prob. 87CPCh. 12 - Prob. 88CPCh. 12 - Prob. 89CPCh. 12 - Assumptions What assumptions are needed to use the...Ch. 12 - Assumptions fail? Refer to the previous exercise....Ch. 12 - Lots of standard deviations Explain carefully the...Ch. 12 - Decrease in home values A Freddie Mac quarterly...Ch. 12 - Population growth Exercise 12.57 about U.S....Ch. 12 - Multiple choice: Interpret r One can interpret r =...Ch. 12 - Multiple choice: Correlation invalid The...Ch. 12 - Multiple choice: Slope and correlation The slope...Ch. 12 - Multiple choice: Regress x on y The regression of...Ch. 12 - Multiple choice: Income and height University of...Ch. 12 - True or false The variables y = annual income...Ch. 12 - Prob. 101CPCh. 12 - Why is there regression toward the mean? Refer to...Ch. 12 - Prob. 103CPCh. 12 - Prob. 104CPCh. 12 - Prob. 105CPCh. 12 - Prob. 106CP
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- The table below shows the number of state-registered automatic weapons and the murder rate for several Northwestern states. 11.6 8.6 6.9 3.9| 2.4 2.2 2.6 0.6 14 11.2 10 7.5 6.1 6 6.3 4.3 x = thousands of automatic weapons y = murders per 100,000 residents This data can be modeled by the equation y = 0.86x + 4.01. Use this equation to answer the following; A) How many murders per 100,000 residents can be expected in a state with 1.4 thousand automatic weapons? Answer = Round to 3 decimal places. B) How many murders per 100,000 residents can be expected in a state with 5.1 thousand automatic weapons? Answer = Round to 3 decimal places.arrow_forwardThe data show the chest size and weight of several bears. Find the regression equation, letting chest size be the independent (x) variable. Then find the best predicted weight of a bear with a chest size of 63 inches. Is the result close to the actual weight of 522 pounds? Use a significance level of 0.05. Chest size (inches) 58 50 65 59 59 48 D 414 312 499 450 456 260 Weight (pounds) Click the icon to view the critical values of the Pearson correlation coefficient r. What is the regression equation? y=+x (Round to one decimal place as needed.) What is the best predicted weight of a bear with a chest size of 63 inches? The best predicted weight for a bear with a chest size of 63 inches is pounds. (Round to one decimal place as needed.) Is the result close to the actual weight of 522 pounds? O A. This result is not very close to the actual weight of the bear. O B. This result is exactly the same as the actual weight of the bear. O C. This result is close to the actual weight of the…arrow_forwardThe data show the chest size and weight of several bears. Find the regression equation, letting chest size be the independent (x) variable. Then find the best predicted weight of a bear with a chest size of 50 inches. Is the result close to the actual weight of 427 pounds? Use a significance level of 0.05. Chest size (inches) Weight (pounds) 49 368 51 382 53 420 61 481 57 457 45 287 E Click the icon to view the critical values of the Pearson correlation coefficient r. see score see score What is the regression equation? y = + x (Round to one decimal place as needed.) What is the best predicted weight of a bear with a chest size of 50 inches? The best predicted weight for a bear with a chest size of 50 inches is pounds. (Round to one decimal place as needed.) Is the result close to the actual weight of 427 pounds? O A. This result is very close to the actual weight of the bear O B. This result is exactly the same as the actual weight of the bear O C. This result is close to the actual…arrow_forward
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