Essentials of Statistics, Books a la Carte Edition (5th Edition)
5th Edition
ISBN: 9780321926739
Author: Mario F. Triola
Publisher: PEARSON
expand_more
expand_more
format_list_bulleted
Concept explainers
Videos
Textbook Question
Chapter 10.3, Problem 32BSC
Large Data Sets. Exercises 29-32 use the same Appendix B data sets as Exercises 29-32 in Section 10-2. In each case, find the regression equation, letting the first variable be the predictor (x) variable. Find the indicated predicted values following the prediction procedure summarized in Figure 10-5.
32. Earthquakes Refer to Data Set 16 in Appendix B and use the magnitudes and depths from the earthquakes. Find the best predicted depth of an earthquake with a magnitude of 1.50.
Expert Solution & Answer
Want to see the full answer?
Check out a sample textbook solution
Students have asked these similar questions
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
Section 10.2
Question #9
The data show the bug chirps per minute at different temperatures. Find the regression equation, letting the first variable be the independent (x) variable. Find the best predicted temperature for a time when a bug is chirping at the rate of 3000 chirps per minute. Use a significance level of 0.05. What is wrong with this predicted value?
Chirps in 1 min
981
1023
1074
1101
1203
874
Temperature
(°F)
83
79.4
80.9
82.8
92.3
72.8
What is the regression equation?
y= ___________+ ___________x
(Round the x-coefficient to four decimal places as needed. Round the constant to two decimal places as needed.)
What is the best predicted temperature for a time when a bug is chirping at the rate of 3000 chirps per minute?
The best predicted temperature when a bug is chirping at
3000 chirps per minute is _________°F.
(Round to one decimal place as needed.)
Section 10.2
Question #10
Find the regression equation, letting overhead width be the predictor (x) variable. Find the best predicted weight of a seal if the overhead width measured from a photograph is 1.7cm. Can the prediction be correct? What is wrong with predicting the weight in this case? Use a significance level of 0.05.
Overhead Width (cm)
7.4
7.3
9.3
7.7
8.8
8.4
Weight (kg)
135
161
238
146
212
203
View the critical values of the Pearson correlation coefficient r.
Critical values of the pearson correlation coefficient r
n
α=0.05
α=0.01
NOTE: To test H0: ρ=0 against H1: ρ≠0, reject H0 if the absolute value of r is greater than the critical value in the table.
4
0.950
0.990
5
0.878
0.959
6
0.811
0.917
7
0.754
0.875
8
0.707
0.834
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…
Chapter 10 Solutions
Essentials of Statistics, Books a la Carte Edition (5th Edition)
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 - Regression If the methods of this section arc used...Ch. 10.4 - Level of Measurement Which of the levels of...Ch. 10.4 - Prob. 3BSCCh. 10.4 - Prob. 4BSCCh. 10.4 - Prob. 5BSCCh. 10.4 - Prob. 6BSCCh. 10.4 - Prob. 7BSCCh. 10.4 - Testing for Rank Correlation. In Exercises 7-12,...Ch. 10.4 - Prob. 9BSCCh. 10.4 - Testing for Rank Correlation. In Exercises 7-12,...Ch. 10.4 - Prob. 11BSCCh. 10.4 - Prob. 12BSCCh. 10.4 - Appendix B Data Sets. In Exercises 13-16, use the...Ch. 10.4 - Prob. 14BSCCh. 10.4 - Appendix B Data Sets. In Exercises 13-16, use the...Ch. 10.4 - Prob. 16BSCCh. 10.4 - Prob. 17BBCh. 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_forwardFind the equation of the regression line for the following data set. x 1 2 3 y 0 3 4arrow_forwardWhat does the y -intercept on the graph of a logistic equation correspond to for a population modeled by that equation?arrow_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_forwardPart I. Run two regressions in Excel using the provided Excel file “Layoffs”.The Excel file Layoffs provides data on 50 manufacturing workers who lost their jobs due to layoffs. The data includes the following list of variables:Weeks – the number of weeks a manufacturing worker has been without a jobAge – the age of the workerEducation – the number of years of education of the workerMarried – a dummy variable, equal to 1 if the worker is married, 0 otherwiseHead – a dummy variable, equal to 1 if the worker is a head of household, 0 otherwiseTenure – the number of years on the previous jobManager – a dummy variable, equal to 1 if the worker had a management occupation, 0 otherwise Sales – a dummy variable, equal to 1 if the worker had an occupation in sales, 0 otherwise 1. Run a simple regression with a dependent variable Weeks and an independent variable Age. Create the regular and standardized residual plots for the simple regression. 2. Run a multiple regression with a dependent…arrow_forwardFind 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. Height, x Stories, y (a) x = 499 feet (c) x = 798 feet (b) x= 646 feet (d) x = 734 feet 766 620 520 508 494 484 51 46 44 42 39 37 Find the regression equation. y x+ (O (Round the slope to three decimal places as needed. Round the y-intercept to two decimal places as needed.) Choose the correct graph below. O A. O B Oc. OD 60- 60- 60- 60 - 04 04 0- 800 800 800 Height (feet) 800 Height (feet) Height (feet) Height (feet) (a) Predict the value of y for x= 499. Choose the correct answer below. O A. 50 O B. 46 O C. 40 O D. not meaningful (b) Predict the value of y for x=646, Choose the correct…arrow_forward
arrow_back_ios
arrow_forward_ios
Recommended textbooks for you
Linear Algebra: A Modern IntroductionAlgebraISBN:9781285463247Author:David PoolePublisher:Cengage Learning
Algebra & Trigonometry with Analytic GeometryAlgebraISBN:9781133382119Author:SwokowskiPublisher:Cengage
Big Ideas Math A Bridge To Success Algebra 1: Stu...AlgebraISBN:9781680331141Author:HOUGHTON MIFFLIN HARCOURTPublisher:Houghton Mifflin Harcourt
Glencoe Algebra 1, Student Edition, 9780079039897...AlgebraISBN:9780079039897Author:CarterPublisher:McGraw Hill
College AlgebraAlgebraISBN:9781305115545Author:James Stewart, Lothar Redlin, Saleem WatsonPublisher:Cengage Learning
Trigonometry (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
Glencoe Algebra 1, Student Edition, 9780079039897...
Algebra
ISBN:9780079039897
Author:Carter
Publisher:McGraw Hill
College Algebra
Algebra
ISBN:9781305115545
Author:James Stewart, Lothar Redlin, Saleem Watson
Publisher:Cengage Learning
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