 7.1: Association. Suppose you were to collect data foreach pair of varia...
 7.2: Association. Suppose you were to collect data foreach pair of varia...
 7.3: Association. Suppose you were to collect data foreach pair of varia...
 7.4: Association. Suppose you were to collect data for eachpair of varia...
 7.5: Scatterplots. Which of the scatterplots at the top of thenext colum...
 7.6: Scatterplots. Which of the scatterplots below showa) little or no a...
 7.7: Performance IQ scores vs. brain size. A study examinedbrain size (m...
 7.8: Kentucky Derby 2006. The fastest horse in KentuckyDerby history was...
 7.9: Firing pottery. A ceramics factory can fire eight largebatches of p...
 7.10: Coffee sales. Owners of a new coffee shop trackedsales for the firs...
 7.11: Matching. Here are several scatterplots. The calculatedcorrelations...
 7.12: Matching. Here and on the next page are several scatterplots. The ...
 7.13: Politics. A candidate for office claims that there is acorrelation ...
 7.14: Car thefts. The National Insurance Crime Bureau reports that Honda...
 7.15: Roller coasters. Roller coasters get all their speed bydropping dow...
 7.16: Antidepressants. A study compared the effectivenessof several antid...
 7.17: Hard water. In a study of streams in the AdirondackMountains, the f...
 7.18: Traffic headaches. A study of traffic delays in 68 U.S.cities found...
 7.19: Cold nights. Is there an association between time ofyear and the ni...
 7.20: Association. A researcher investigating the associationbetween two ...
 7.21: Prediction units. The errors in predicting hurricanetracks (examine...
 7.22: More predictions. Hurricane Katrinas hurricane forcewinds extended ...
 7.23: Correlation errors. Your Economics instructor assignsyour class to ...
 7.24: More correlation errors. Students in the Economicsclass discussed i...
 7.25: Height and reading. A researcher studies children inelementary scho...
 7.26: Cellular telephones and life expectancy. A survey ofthe worlds nati...
 7.27: Correlation conclusions I. The correlation betweenAge and Income as...
 7.28: Correlation conclusions II. The correlation betweenFuel Efficiency ...
 7.29: Baldness and heart disease. Medical researchers followed 1435 midd...
 7.30: Sample survey. A polling organization is checking itsdatabase to se...
 7.31: Income and housing. The Office of Federal HousingEnterprise Oversig...
 7.32: Interest rates and mortgages. Since 1980, averagemortgage interest ...
 7.33: Fuel economy 2007. Here are advertised horsepowerratings and expect...
 7.34: Drug abuse. A survey was conducted in the UnitedStates and 10 count...
 7.35: Burgers. Fast food is often considered unhealthy because much of i...
 7.36: Burgers II. In the previous exercise you analyzed theassociation be...
 7.37: Attendance 2006. American League baseball gamesare played under the...
 7.38: Second inning 2006. Perhaps fans are just more interested in teams...
 7.39: Thrills. People who responded to a July 2004 Discovery Channel pol...
 7.40: Vehicle weights. The Minnesota Department ofTransportation hoped th...
 7.41: Planets (more or less). On August 24, 2006, the International Astr...
 7.42: Flights. The number of flights by U.S. Airlines hasgrown rapidly. H...
Solutions for Chapter 7: Scatterplots, Association, and Correlation
Full solutions for Stats: Modeling The World  3rd Edition
ISBN: 9780131359581
Solutions for Chapter 7: Scatterplots, Association, and Correlation
Get Full SolutionsStats: Modeling The World was written by and is associated to the ISBN: 9780131359581. This textbook survival guide was created for the textbook: Stats: Modeling The World , edition: 3. This expansive textbook survival guide covers the following chapters and their solutions. Since 42 problems in chapter 7: Scatterplots, Association, and Correlation have been answered, more than 33589 students have viewed full stepbystep solutions from this chapter. Chapter 7: Scatterplots, Association, and Correlation includes 42 full stepbystep solutions.

Bayesâ€™ estimator
An estimator for a parameter obtained from a Bayesian method that uses a prior distribution for the parameter along with the conditional distribution of the data given the parameter to obtain the posterior distribution of the parameter. The estimator is obtained from the posterior distribution.

Box plot (or box and whisker plot)
A graphical display of data in which the box contains the middle 50% of the data (the interquartile range) with the median dividing it, and the whiskers extend to the smallest and largest values (or some deined lower and upper limits).

Center line
A horizontal line on a control chart at the value that estimates the mean of the statistic plotted on the chart. See Control chart.

Components of variance
The individual components of the total variance that are attributable to speciic sources. This usually refers to the individual variance components arising from a random or mixed model analysis of variance.

Control limits
See Control chart.

Correction factor
A term used for the quantity ( / )( ) 1 1 2 n xi i n ? = that is subtracted from xi i n 2 ? =1 to give the corrected sum of squares deined as (/ ) ( ) 1 1 2 n xx i x i n ? = i ? . The correction factor can also be written as nx 2 .

Covariance
A measure of association between two random variables obtained as the expected value of the product of the two random variables around their means; that is, Cov(X Y, ) [( )( )] =? ? E X Y ? ? X Y .

Critical value(s)
The value of a statistic corresponding to a stated signiicance level as determined from the sampling distribution. For example, if PZ z PZ ( )( .) . ? =? = 0 025 . 1 96 0 025, then z0 025 . = 1 9. 6 is the critical value of z at the 0.025 level of signiicance. Crossed factors. Another name for factors that are arranged in a factorial experiment.

Defect
Used in statistical quality control, a defect is a particular type of nonconformance to speciications or requirements. Sometimes defects are classiied into types, such as appearance defects and functional defects.

Degrees of freedom.
The number of independent comparisons that can be made among the elements of a sample. The term is analogous to the number of degrees of freedom for an object in a dynamic system, which is the number of independent coordinates required to determine the motion of the object.

Design matrix
A matrix that provides the tests that are to be conducted in an experiment.

Error mean square
The error sum of squares divided by its number of degrees of freedom.

Expected value
The expected value of a random variable X is its longterm average or mean value. In the continuous case, the expected value of X is E X xf x dx ( ) = ?? ( ) ? ? where f ( ) x is the density function of the random variable X.

Extra sum of squares method
A method used in regression analysis to conduct a hypothesis test for the additional contribution of one or more variables to a model.

F distribution.
The distribution of the random variable deined as the ratio of two independent chisquare random variables, each divided by its number of degrees of freedom.

Finite population correction factor
A term in the formula for the variance of a hypergeometric random variable.

Fisherâ€™s least signiicant difference (LSD) method
A series of pairwise hypothesis tests of treatment means in an experiment to determine which means differ.

Fractional factorial experiment
A type of factorial experiment in which not all possible treatment combinations are run. This is usually done to reduce the size of an experiment with several factors.

Gamma random variable
A random variable that generalizes an Erlang random variable to noninteger values of the parameter r

Harmonic mean
The harmonic mean of a set of data values is the reciprocal of the arithmetic mean of the reciprocals of the data values; that is, h n x i n i = ? ? ? ? ? = ? ? 1 1 1 1 g .