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
Question
Chapter 10.3, Problem 33BB
To determine
To explain: The reason that the test of the null hypothesis
Expert Solution & Answer
Want to see the full answer?
Check out a sample textbook solution
Students have asked these similar questions
A researcher recorded the number of e-mails received in a month and the number of online purchases made during that month for 50 people with an online presence. The resulting data were used to conduct a hypothesis test to investigate whether the slope of the population regression line relating number of e-mails received to number of online purchases is positive. What are the correct hypotheses for the test?
H0:β1=0Ha:β1≠0H0:β1=0Ha:β1≠0
A
H0:β1=0Ha:β1>0H0:β1=0Ha:β1>0
B
H0:β1=0Ha:β1<0H0:β1=0Ha:β1<0
C
H0:β1>0Ha:β1=0H0:β1>0Ha:β1=0
D
H0:b1=0Ha:b1≠0
E
Which of these options are true?
In a simple linear regression model with one predictor variable, what is the coefficient of determination (R-squared) if the Pearson's correlation coefficient between the predictor and response variable is 0.6?
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_forwardCellular Phone Subscribers The table shows the numbers of cellular phone subscribers y in millions in the United States from 2008 through 2013. Source: CTIA- The Wireless Association Year200820092010201120122013Number,y270286296316326336 (a) Find the least squares regression line for the data. Let x represent the year, with x=8 corresponding to 2008. (b) Use the linear regression capabilities of a graphing utility to find a linear model for the data. How does this model compare with the model obtained in part a? (c) Use the linear model to create a table of estimated values for y. Compare the estimated values with the actual data.arrow_forward24) If the R-square value for a simple linear regression model is 0.80 and the regression line has anegative slope, the correlation coefficient describing the relationship between the two variables is_____________.arrow_forward
- I’m interested in seeing whether or not there is a relationship between the number of hours someone sleeps and traffic accidents. I gather data from the Department of Transportation and get the following data: X (hours of sleep) Y (Traffic accidents) 5 2 5 1 3 4 4 4 2 6 6 3 8 1 9 1 5 0 6 4 Using the above data, create a linear regression equation to predict traffic accidents from hours of sleep. What is the Pearson correlation coefficient? Is it strong or weak? Positive or negative? What is the linear regression equation? (in the form of Y’ = bX + a) How many traffic accidents might we predict with someone that gets 0 hours of sleep? How many traffic accidents might we predict with someone that gets 11 hours of sleep?arrow_forward1. Find the linear regression equation for data. y = x + (Round answer to three decimal places) 2. Find the linear correlation coefficient (r) r = (Round answer to FOUR decimal places) 3. Find the coefficient of determination (r2) r2=arrow_forwardIn a simple linear regression model, the correlation coefficient between x and y is 0.8. What can you say about the strength and direction of the relationship between x and y?arrow_forward
- Using the data given in the Question about Correlation between numbers of commuters and numbers of parking spaces, find the equation of the regression line in which the explanatory variable (or x variable) is the numbers of commuters and the response variable (or y variable) is the numbers of parking spaces.arrow_forwardLook or search for a real-life problem or phenomenon in psychology and consider two important and related variables that have an existing data set. Then perform/ find the following: a.Determine the linear correlation coefficient for the relationship between the two chosen variables. b.Interpret the strength of relationship for the two variables based on the calculated Pearson r value in (a). c.Find the prediction equation of the regression line for the data using the least squares method. d.Construct the scatter plot of the data and the regression line.arrow_forwardThe data in the table represent the number of licensed drivers in various age groups and the number of fatal accidents within the age group by gender. Complete parts (a) through (c) below. Click the icon to view the data table. ... . (a) Find the least-squares regression line for males treating the number of licensed drivers as the explanatory variable, x, and the number of fatal crashes, y, as the response variable. Repeat this procedure for females. Find the least-squares regression line for males. y=x+O Data for licensed drivers by age and gender. %3D (Round the x coefficient to three decimal places as needed. Round the constant to the nearest integer as needed.) Find the least-squares regression line for females. y = Number of Number o X+ Number of Male Fatal Number of Female Fatal (Round the x coefficient to three decimal places as needed. Round the constant to the nearest integer as needed.) Licensed Drivers Crashes Licensed Drivers Crashes (b) Interpret the slope of the…arrow_forward
- Data from 10 Western European countries: summary statistics show that the mean percent of teens that use marijuana is 23.9 with a standard deviation of 15.6. The mean percent of teens that use other drugs is 11.6 with a standard deviation of 10.2. The correlation coefficient is r= 0.93 Find the equation of the least squares regression line considering Marijuana Use as the Explanatory variable and Other Drug Use as the Response variable. O a. ý = 7.43 + 1.42x O b. ý = - 1.42 -0.61x %3D Oc = -2.98 +0.61x %3D O d. ý = 6.10 +2.98xarrow_forwardyour won A regression was run to determine If there Is a relationship between the happiness index (y) and life expectancy In years of a glven country (x). The results of the regression were: ý=a+bx a=-1.711 b=0.162 (a) Write the equation of the Least Squares Regression line of the form (b) Which is a possible value for the correlation coefficient, r? O-0.869 Q-1.197 O1.197 O 0.869 (C) If a country increases its life expectancy, the happiness index will O decrease Oincrease (d) If the life expectancy is increased by 4.5 years in a certain country, how much will the happiness Index change? Round to two decimal places.arrow_forwardThe accompanying data represent the weights of various domestic cars and their gas mileages in the city. The linear correlation coefficient between the weight of a car and its miles per gallon in the city is r= - 0.977. The least-squares regression line treating weight as the explanatory variable and miles per gallon as the response variable is -0.0061x +41.3297. Complete parts (a) and (b) below. V = Click the icon to view the data table. (a) What proportion of the variability in miles per gallon is explained by the relation between weight of the car and miles per gallon? The proportion of the variability in miles per gallon explained by the relation between weight of the car and miles per gallon is %. (Round to one decimal place as needed.) (b) Interpret the coefficient of determination. % of the variance in is by the linear model. (Round to one decimal place as needed.) Data Table Full data set Miles per Miles per Weight (pounds), x Weight (pounds), x Car Car Gallon, y Gallon, y 1…arrow_forward
arrow_back_ios
SEE MORE QUESTIONS
arrow_forward_ios
Recommended textbooks for you
Glencoe Algebra 1, Student Edition, 9780079039897...AlgebraISBN:9780079039897Author:CarterPublisher:McGraw Hill
Linear Algebra: A Modern IntroductionAlgebraISBN:9781285463247Author:David PoolePublisher:Cengage Learning
Big Ideas Math A Bridge To Success Algebra 1: Stu...AlgebraISBN:9781680331141Author:HOUGHTON MIFFLIN HARCOURTPublisher:Houghton Mifflin Harcourt
Elementary Linear Algebra (MindTap Course List)AlgebraISBN:9781305658004Author:Ron LarsonPublisher:Cengage Learning
Functions and Change: A Modeling Approach to Coll...AlgebraISBN:9781337111348Author:Bruce Crauder, Benny Evans, Alan NoellPublisher:Cengage Learning
Glencoe Algebra 1, Student Edition, 9780079039897...
Algebra
ISBN:9780079039897
Author:Carter
Publisher:McGraw Hill
Linear Algebra: A Modern Introduction
Algebra
ISBN:9781285463247
Author:David Poole
Publisher:Cengage Learning
Big Ideas Math A Bridge To Success Algebra 1: Stu...
Algebra
ISBN:9781680331141
Author:HOUGHTON MIFFLIN HARCOURT
Publisher:Houghton Mifflin Harcourt
Elementary Linear Algebra (MindTap Course List)
Algebra
ISBN:9781305658004
Author:Ron Larson
Publisher:Cengage Learning
Functions and Change: A Modeling Approach to Coll...
Algebra
ISBN:9781337111348
Author:Bruce Crauder, Benny Evans, Alan Noell
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