Elementary Statistics
12th Edition
ISBN: 9780321836960
Author: Mario F. Triola
Publisher: PEARSON
expand_more
expand_more
format_list_bulleted
Concept explainers
Textbook Question
Chapter 10.3, Problem 24BSC
Regression and Predictions. Exercises 13-28 use the same data sets as Exercises 13-28 in Section 10-1. In each case, find the regression equation, letting the first variable be the predictor (x) variable, bind the indicated predicted value by following the prediction procedure summarized in Figure 10-5 on page 493.
22. Crickets and Temperature Find the best predicted temperature at a time when a cricket chirps 3000 limes in 1 minute. What is wrong with this predicted temperature?
Chirps in 1 min | 882 | 1188 | 1104 | 864 | 1200 | 1032 | 960 | 900 |
Temperature (°F) | 69.7 | 93.3 | 84.3 | 76.3 | 88.6 | 82.6 | 71.6 | 79.6 |
Expert Solution & Answer
Want to see the full answer?
Check out a sample textbook solutionStudents have asked these similar questions
Number 25
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
A researcher calculates a regression equation to predict an insurance premium based
on a person's age. One person in the sample was observed to have a premium of
$500 at 20 years old, but the predicted value was $400. VWhat is the value of the
residual for this person?
Next Page
Page 10 of 14
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
Knowledge Booster
Learn more about
Need a deep-dive on the concept behind this application? Look no further. Learn more about this topic, statistics and related others by exploring similar questions and additional content below.Similar questions
- 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?arrow_forwardWhat does the y -intercept on the graph of a logistic equation correspond to for a population modeled by that equation?arrow_forwardFind the equation of the regression line for the following data set. x 1 2 3 y 0 3 4arrow_forward
- If your graphing calculator is capable of computing a least-squares sinusoidal regression model, use it to find a second model for the data. Graph this new equation along with your first model. How do they compare?arrow_forwardFind the regression equation, letting the first variable be the predictor (x) variable. Using the listed actress/actor ages in various years, find the best predicted age of the Best Actor winner given that the age of the Best Actress winner that year is 28 years. Is the result within 5 years of the actual Best Actor winner, whose age was 39 years? Best Actress: 29; 29; 28; 64; 34; 35; 44; 29; 65; 23; 46; 54 Best Actor: 43; 39; 39; 43; 53; 46; 58; 52; 39; 56; 44; 35 Find the equation of the regression line. y(carety)=____ + ______x The best predicted age of the Best Actor winner given that the age of the Best Actress winner that year is 28 years is _____ years old.arrow_forwardFind the regression equation Student 1 4 5 6 7 8 9 10 Placement Exam 83 89 92 77 81 81 85 85 87 84 GWA 83 85 90 80 83 85 87 84 90 85 Select the correct response: y=35.39-0.5901x y=35.39x+0.5901 y=0.5901x-35.39 y=35.39+0.5901x 3. 2.arrow_forward
- Which of the figures shows a violation of regression model assumption? 1 2 3 4arrow_forwardA) Compute the last-squares regression line for predicting US emission from NON US - emissions. b) If the non-US emission differ by 0.2 from one year to the next by how much would you predict the US- emission to differ?arrow_forwardThe table below shows the amounts of crude oil (in thousands of barrels per day) produced by a country and the amounts of crude oil (in thousands of barrels per day) imported by a country, for the last seven years. Construct and interpret a 98% prediction interval for the amount of crude oil imported by the this country when the amount of crude oil produced by the country is 5,583 thousand barrels per day. The equation of the regression line is V = - 1.116x + 15,810.670. Oil produced, x Oil imported, y 5,773 5,694 5,638 5,481 5,165 5,051 5,010 9,345 9,132 9,616 10,049 10,152 10,165 10,030 Construct and interpret a 98% prediction interval for the amount of crude oil imported when the amount of crude oil produced by the country is 5,583 thousand barrels per day. Select the correct choice below and fill in the answer boxes to complete your choice. (Round to the nearest cent as needed.) O A. There is a 98% chance that the predicted amount of oil imported is between and thousand barrels,…arrow_forward
- A time series regression equation measuring the number of surfboards sold by a surfboard manufacturing company in Australia is given below: Y = 35 + 4Q 1+0.5Q3 + 8Q4+ 3t with tin quarters and the first observation of the dataset is December 2015 and Q1 is the indicator variable for March, Q3 is the indicator variable for September and Q4 is the indicator variable for December. Which of the following statements is correct? There is a mistake with this regression model because all the coefficients of the indicator variables are positive. There is no indicator variable for the June quarter in this model because there is no seasonal effect in June due to winter in Australia. There is a mistake with this model because the indicator variable for the June quarter has been left out. There is no indicator variable for the June quarter because the model has an intercept and including the June quarter would result in perfect multicollinearity.arrow_forward9. A wildlife researcher is interested in predicting an alligator’s weight (in pounds) based on its length (in inches). Data was obtained from a large random sample of alligators, and the regression equation turned out as follows: Predicted weight = –393 + 5.9 (length) Which one of the following statements is a correct interpretation of this equation? 1. As weight increases by one pound, length is predicted to decrease by 393 inches. 2. As length increases by one inch, weight is predicted to increase by 5.9 pounds. 3. As weight increases by one pound, length is predicted to increase by 5.9 inches. 4. As length increases by one inch, weight is predicted to decrease by 393 pounds. 5. Approximately 5.9% of the variability in weight can be explained by the regression equation.arrow_forwardTwo variable are found to have a strong negative linear correlation. Pick the regression equation that best fits this scenario. y=0.82x−28 ˆy=0.32x−28 y= -0.82x+28 ˆy= -0.32x+28arrow_forward
arrow_back_ios
SEE MORE QUESTIONS
arrow_forward_ios
Recommended textbooks for you
- Linear Algebra: A Modern IntroductionAlgebraISBN:9781285463247Author:David PoolePublisher:Cengage LearningAlgebra & Trigonometry with Analytic GeometryAlgebraISBN:9781133382119Author:SwokowskiPublisher:CengageBig Ideas Math A Bridge To Success Algebra 1: Stu...AlgebraISBN:9781680331141Author:HOUGHTON MIFFLIN HARCOURTPublisher:Houghton Mifflin Harcourt
- College AlgebraAlgebraISBN:9781305115545Author:James Stewart, Lothar Redlin, Saleem WatsonPublisher:Cengage LearningGlencoe Algebra 1, Student Edition, 9780079039897...AlgebraISBN:9780079039897Author:CarterPublisher:McGraw HillTrigonometry (MindTap Course List)TrigonometryISBN:9781305652224Author:Charles P. McKeague, Mark D. TurnerPublisher:Cengage Learning
Linear Algebra: A Modern Introduction
Algebra
ISBN:9781285463247
Author:David Poole
Publisher:Cengage Learning
Algebra & Trigonometry with Analytic Geometry
Algebra
ISBN:9781133382119
Author:Swokowski
Publisher:Cengage
Big Ideas Math A Bridge To Success Algebra 1: Stu...
Algebra
ISBN:9781680331141
Author:HOUGHTON MIFFLIN HARCOURT
Publisher:Houghton Mifflin Harcourt
College Algebra
Algebra
ISBN:9781305115545
Author:James Stewart, Lothar Redlin, Saleem Watson
Publisher:Cengage Learning
Glencoe Algebra 1, Student Edition, 9780079039897...
Algebra
ISBN:9780079039897
Author:Carter
Publisher:McGraw Hill
Trigonometry (MindTap Course List)
Trigonometry
ISBN:9781305652224
Author:Charles P. McKeague, Mark D. Turner
Publisher:Cengage Learning
Correlation Vs Regression: Difference Between them with definition & Comparison Chart; Author: Key Differences;https://www.youtube.com/watch?v=Ou2QGSJVd0U;License: Standard YouTube License, CC-BY
Correlation and Regression: Concepts with Illustrative examples; Author: LEARN & APPLY : Lean and Six Sigma;https://www.youtube.com/watch?v=xTpHD5WLuoA;License: Standard YouTube License, CC-BY