 8.1E: Credit card spending An analysis of spending by a sample of credit ...
 8.2E: Revenue and talent cost A concert production company examined its r...
 8.3E: Market segments The analyst in Exercise 1 tried fitting the regress...
 8.4E: Revenue and ticket sales The concert production company of Exercise...
 8.5E: Cell phone costs Noting a recent study predicting the increase in c...
 8.6E: Stopping times Using data from 20 compact cars, a consumer group de...
 8.7E: Revenue and large venues A regression of Total Revenue on Ticket Sa...
 8.8E: Revenue and advanced sales The production company of Exercise 7 off...
 8.9E: Abalone Abalones are edible sea snails that include over 100 specie...
 8.10E: Abalone again The researcher in Exercise is content with the second...
 8.11E: Skinned knees There is a strong correlation between the temperature...
 8.12E: Cell phones and life expectancy The correlation between cell phone ...
 8.13E: Grading A team of Calculus teachers is analyzing studentscores on a...
 8.14E: Average GPA An athletic director proudly states that he has used th...
 8.15E: Marriage age 2010 Is there evidence that the age at which women get...
 8.16E: Smoking 2011 The Centers for Disease Control and Prevention track c...
 8.17E: Human Development Index 2012 The United Nations Development Program...
 8.18E: HDI 2012 revisited The United Nations Development Programme (UNDP) ...
 8.19E: Good model? In justifying his choice of a model, a student wrote, “...
 8.20E: Bad model? A student who has created a linear model is disappointed...
 8.21E: Movie dramas Here’s a scatterplot of the production budgets (in mil...
 8.22E: Smoking 2011, women and men In Exercise 16, we examined the percent...
 8.23E: Oakland passengers 2013 The scatterplot below shows the number of p...
 8.24E: Tracking hurricanes 2012 In a previous chapter, we saw data on the ...
 8.25E: Unusual points Each of the four scatterplots that follow shows a cl...
 8.26E: More unusual points Each of the following scatterplots shows a clus...
 8.27E: The extra point The scatterplot shows five blue data points at the ...
 8.28E: The extra point revisited The original five points in Exercise prod...
 8.29E: What’s the cause? Suppose a researcher studying health issues measu...
 8.30E: What’s the effect? A researcher studying violent behavior in elemen...
 8.31E: Reading To measure progress in reading ability, students at an elem...
 8.32E: Grades A college admissions officer, defending the college’s use of...
 8.33E: Heating After keeping track of his heating expenses for several win...
 8.34E: Speed How does the speed at which you drive affect your fuel econom...
 8.35E: Interest rates 2014 Here’s a plot showing the federal rate on 3mon...
 8.36E: Marriage age, 2011 The graph shows the ages of both men and women a...
 8.37E: Interest rates 2014 revisited In Exercise 35, you investigated the ...
 8.38E: Marriage age 2011 again Has the trend of decreasing difference in a...
 8.39E: Gestation For women, pregnancy lasts about 9 months. In other speci...
 8.40E: Swim the lake 2013 People swam across Lake Ontario 52 times between...
 8.41E: Elephants and hippos We removed humans from the scatterplot in Exer...
 8.42E: Another swim 2013 In Exercise 40, we saw that Vicki Keith’s roundt...
 8.43E: Marriage age 2010 revisited Suppose you wanted to predict the trend...
 8.44E: Bridges covered In Chapter 7, (Data in Tompkins County Bridges 2014...
 8.45E: Life expectancy 2013 Data for 26 Western Hemisphere countries can b...
 8.46E: Tour de France 2014 We met the Tour de France data set in Chapter 1...
 8.47E: Inflation 2011 The Consumer Price Index (CPI) tracks the prices of ...
Solutions for Chapter 8: Stats: Data and Models 4th Edition
Full solutions for Stats: Data and Models  4th Edition
ISBN: 9780321986498
Solutions for Chapter 8
Get Full SolutionsSince 47 problems in chapter 8 have been answered, more than 37308 students have viewed full stepbystep solutions from this chapter. This expansive textbook survival guide covers the following chapters and their solutions. Stats: Data and Models was written by and is associated to the ISBN: 9780321986498. Chapter 8 includes 47 full stepbystep solutions. This textbook survival guide was created for the textbook: Stats: Data and Models , edition: 4.

Bernoulli trials
Sequences of independent trials with only two outcomes, generally called “success” and “failure,” in which the probability of success remains constant.

Binomial random variable
A discrete random variable that equals the number of successes in a ixed number of Bernoulli trials.

Categorical data
Data consisting of counts or observations that can be classiied into categories. The categories may be descriptive.

Central composite design (CCD)
A secondorder response surface design in k variables consisting of a twolevel factorial, 2k axial runs, and one or more center points. The twolevel factorial portion of a CCD can be a fractional factorial design when k is large. The CCD is the most widely used design for itting a secondorder model.

Central tendency
The tendency of data to cluster around some value. Central tendency is usually expressed by a measure of location such as the mean, median, or mode.

Conditional mean
The mean of the conditional probability distribution of a random variable.

Conidence coeficient
The probability 1?a associated with a conidence interval expressing the probability that the stated interval will contain the true parameter value.

Conidence interval
If it is possible to write a probability statement of the form PL U ( ) ? ? ? ? = ?1 where L and U are functions of only the sample data and ? is a parameter, then the interval between L and U is called a conidence interval (or a 100 1( )% ? ? conidence interval). The interpretation is that a statement that the parameter ? lies in this interval will be true 100 1( )% ? ? of the times that such a statement is made

Contour plot
A twodimensional graphic used for a bivariate probability density function that displays curves for which the probability density function is constant.

Control chart
A graphical display used to monitor a process. It usually consists of a horizontal center line corresponding to the incontrol value of the parameter that is being monitored and lower and upper control limits. The control limits are determined by statistical criteria and are not arbitrary, nor are they related to speciication limits. If sample points fall within the control limits, the process is said to be incontrol, or free from assignable causes. Points beyond the control limits indicate an outofcontrol process; that is, assignable causes are likely present. This signals the need to ind and remove the assignable causes.

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 .

Decision interval
A parameter in a tabular CUSUM algorithm that is determined from a tradeoff between false alarms and the detection of assignable causes.

Designed experiment
An experiment in which the tests are planned in advance and the plans usually incorporate statistical models. See Experiment

Discrete distribution
A probability distribution for a discrete random variable

Discrete random variable
A random variable with a inite (or countably ininite) range.

Dispersion
The amount of variability exhibited by data

Estimate (or point estimate)
The numerical value of a point estimator.

Event
A subset of a sample space.

Firstorder model
A model that contains only irstorder terms. For example, the irstorder response surface model in two variables is y xx = + ?? ? ? 0 11 2 2 + + . A irstorder model is also called a main effects model

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