Elementary Statistics (13th Edition)
13th Edition
ISBN: 9780134462455
Author: Mario F. Triola
Publisher: PEARSON
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Chapter 10.4, Problem 15BSC
Appendix B Data Sets. In Exercises 13-16, refer to the indicated data set in Appendix B and use technology to obtain results.
15. Predicting IQ Score Refer to Data Set 8 “IQ and Brain Size” in Appendix B and find the best regression equation with IQ score as the response (y) variable. Use predictor variables of brain volume and/or body weight. Why is this equation best? Based on these results, can we predict someone’s IQ score if we know their brain volume and body weight? Based on these results, does it appear that people with larger brains have higher IQ scores?
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Create the regression equations based on the research model below!
A magazine publishes restaurant ratings for various locations around the world. The magazine rates the restaurants for food, decor, service, and the cost per person. Develop a regression model to predict the cost per person, based on a variable that represents the sum of the three
ratings. The magazine has compiled the accompanying table of this summated ratings variable and the cost per person for 25 restaurants in a major city. Complete parts (a) through (e) below.
Click the icon to view the table of summated ratings and cost per person.
.....
a. Construct a scatter plot. Choose the correct graph below.
A.
Ов.
С.
D.
ACost ($)
90-
ACost ($)
90-
ACost ($)
90-
ACost ($)
90-
0-
0-
90
90
90
90
Rating
Rating
Rating
Rating
b. Assuming a linear relationship, use the least-squares method to compute the regression coefficients b, and b,.
bo = and b,
(Round to two decimal places as needed.)
c. Interpret the meaning of the Y-intercept, bo, and the slope, b,. Choose the correct answer below.
O A.…
Chapter 10 Solutions
Elementary Statistics (13th Edition)
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- What is regression analysis? Describe the process of performing regression analysis on a graphing utility.arrow_forwardUsing your graphing calculator, make a scatter plot of the data from the table. Then graph your model from Question 2 along with the data. How well does your model fit the data? What could you do to try to improve your model?arrow_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
- Noise and Intelligibility Audiologists study the intelligibility of spoken sentences under different noise levels. Intelligibility, the MRT score, is measured as the percent of a spoken sentence that the listener can decipher at a cesl4ain noise level in decibels (dB). The table shows the results of one such test. (a) Make a scatter plot of the data. (b) Find and graph the regression line. (c) Find the correlation coefficient. Is a linear model appropriate? (d) Use the linear model in put (b) to estimate the intelligibility of a sentence at a 94-dB noise level.arrow_forwardHOW DO YOU SEE IT? Discuss how well a linear model approximates the data shown in each scatter plot.arrow_forwardA magazine publishes restaurant ratings for various locations around the world. The magazine rates the restaurants for food, decor, service, and the cost per person. Develop a regression model to predict the cost per person, based on a variable that represents the sum of the three ratings. The magazine has compiled the accompanying table of this summated ratings variable and the cost per person for 25 restaurants in a major city. Complete parts (a) through (e) below. Click the icon to view the table of summated ratings and cost per person. a. Construct a scatter plot. Choose the correct graph below. O A. Ов. OC. OD. ACost ($) 90- Q A Cost ($) 904 A Cost ($) 90- ACost ($) 90- 0- 0- 0- 0- 90 Rating 90 Rating 90 90 Rating Rating Summated ratings and cost per person b. Assuming a linear relationship, use the least-squares method to compute the regression coefficients bo and b,. bo =D and b, =O (Round to two decimal places as needed.) Summated Rating Cost ($ per person)|9 c. Interpret the…arrow_forward
- A magazine publishes restaurant ratings for various locations around the world. The magazine rates the restaurants for food, decor, service, and the cost per person. Develop a regression model to predict the cost per person, based on a variable that represents the sum of the three ratings. The magazine has compiled the accompanying table of this summated ratings variable and the cost per person for 25 restaurants in a major city. Complete parts (a) through (e) below. Click the icon to view the table of summated ratings and cost per person. a. Construct a scatter plot. Choose the correct graph below. O A. 90+ 0 0 Cost ($) The M Rating 90 Q O B. A Cost (5) 90+ 0 H +4 Alpe Rating 90 Q b. Assuming a linear relationship, use the least-squares method to compute the regression coefficients bo and b₁. bo= and b₁ = (Round to two decimal places as needed.) C O C. 90+ 0- Cost (S) HA Rating 90 Q Summated Ratings and Cost Per Person Summated Rating Cost ($ per person) 40 48 60 61 42 40 43 55 67 69…arrow_forwardIn Washington, DC (not included in the data set) in 2020 the percentage of people living in poverty was 15.0%. Use the regression equation to predict the corresponding value of the response variable (be sure to show your work). Write a sentence to interpret your result. Is your prediction an example of interpolation or extrapolation? Explain.arrow_forward
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