 4.1: Histogram. Find a histogram that shows the distribution of a varia...
 4.2: Not a histogram. Find a graph other than a histogramthat shows the ...
 4.3: In the news. Find an article in a newspaper, a magazine, or the In...
 4.4: In the news II. Find an article in a newspaper, a magazine, or the...
 4.5: Thinking about shape. Would you expect distributions of these vari...
 4.6: More shapes. Would you expect distributions of thesevariables to be...
 4.7: Sugar in cereals. The histogram displays the sugarcontent (as a per...
 4.8: Singers. The display shows the heights of some of thesingers in a c...
 4.9: Vineyards. The histogram shows the sizes (in acres) of36 vineyards ...
 4.10: Run times. One of the authors collected the times (inminutes) it to...
 4.11: Heart attack stays. The histogram shows the lengths ofhospital stay...
 4.12: Emails. A university teacher saved every email received from stu...
 4.13: Super Bowl points. How many points do footballteams score in the Su...
 4.14: Super Bowl wins. In the Super Bowl, by how manypoints does the winn...
 4.15: Standard deviation I. For each lettered part, a throughc, examine t...
 4.16: Standard deviation II. For each lettered part, athrough c, examine ...
 4.17: Pizza prices. The histogram shows the distributionof the prices of ...
 4.18: Neck size. The histogram shows the neck sizes (ininches) of 250 men...
 4.19: Pizza prices again. Look again at the histogram of thepizza prices ...
 4.20: Neck sizes again. Look again at the histogram ofmens neck sizes in ...
 4.21: Movie lengths. The histogram shows the runningtimes in minutes of 1...
 4.22: Golf drives. The display shows the average drivedistance (in yards)...
 4.23: Movie lengths II. Exercise 21 looked at the runningtimes of movies ...
 4.24: Golf drives II. Exercise 22 looked at distances PGAgolfers can hit ...
 4.25: Mistake. A clerk entering salary data into a companyspreadsheet acc...
 4.26: Cold weather. A meteorologist preparing a talk aboutglobal warming ...
 4.27: Movie budgets. The histogram shows the budgets(in millions of dolla...
 4.28: Sick days. During contract negotiations, a companyseeks to change t...
 4.29: Payroll. A small warehouse employs a supervisor at$1200 a week, an ...
 4.30: Singers. The frequency table shows the heights (ininches) of 130 me...
 4.31: Gasoline. In March 2006, 16 gas stations in GrandJunction, CO, post...
 4.32: The Great One. During his 20 seasons in the NHL,Wayne Gretzky score...
 4.33: States. The stemandleaf display shows populations ofthe 50 states...
 4.34: Wayne Gretzky. In Exercise 32, you examined thenumber of games play...
 4.35: Home runs. The stemandleaf display shows the number of home runs...
 4.36: Bird species. The Cornell Lab of Ornithology holds anannual Christm...
 4.37: Hurricanes 2006. The data below give the number ofhurricanes classi...
 4.38: Horsepower. Create a stemandleaf display for thesehorsepowers of ...
 4.39: Home runs again. Students were asked to make ahistogram of the numb...
 4.40: Return of the birds. Students were given theassignment to make a hi...
 4.41: Acid rain. Two researchers measured the pH (a scaleon which a value...
 4.42: Marijuana 2003. In 2003 the Council of Europepublished a report ent...
 4.43: Final grades. A professor (of something other than Statistics!) di...
 4.44: Final grades revisited. After receiving many complaints about his ...
 4.45: Zip codes. HolesRUs, an Internet company that sellspiercing jewel...
 4.46: Zip codes revisited. Here are some summary statisticsto go with the...
 4.47: Math scores 2005. The National Center for EducationStatistics (http...
 4.48: Boomtowns. In 2006, Inc. magazine (www.inc.com)listed its choice of...
 4.49: Gasoline usage 2004. The California Energy Commission (www.energy....
 4.50: Prisons 2005. A report from the U.S. Department ofJustice (www.ojp....
Solutions for Chapter 4: Displaying and Summarizing Quantitative Data
Full solutions for Stats: Modeling The World  3rd Edition
ISBN: 9780131359581
Solutions for Chapter 4: Displaying and Summarizing Quantitative Data
Get Full SolutionsThis expansive textbook survival guide covers the following chapters and their solutions. This textbook survival guide was created for the textbook: Stats: Modeling The World , edition: 3. Stats: Modeling The World was written by and is associated to the ISBN: 9780131359581. Chapter 4: Displaying and Summarizing Quantitative Data includes 50 full stepbystep solutions. Since 50 problems in chapter 4: Displaying and Summarizing Quantitative Data have been answered, more than 44582 students have viewed full stepbystep solutions from this chapter.

Acceptance region
In hypothesis testing, a region in the sample space of the test statistic such that if the test statistic falls within it, the null hypothesis cannot be rejected. This terminology is used because rejection of H0 is always a strong conclusion and acceptance of H0 is generally a weak conclusion

Attribute
A qualitative characteristic of an item or unit, usually arising in quality control. For example, classifying production units as defective or nondefective results in attributes data.

Bayes’ 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.

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

Biased estimator
Unbiased estimator.

Bivariate distribution
The joint probability distribution of two random variables.

Block
In experimental design, a group of experimental units or material that is relatively homogeneous. The purpose of dividing experimental units into blocks is to produce an experimental design wherein variability within blocks is smaller than variability between blocks. This allows the factors of interest to be compared in an environment that has less variability than in an unblocked experiment.

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.

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

Continuous random variable.
A random variable with an interval (either inite or ininite) of real numbers for its range.

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.

Crossed factors
Another name for factors that are arranged in a factorial experiment.

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.

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

Distribution function
Another name for a cumulative distribution function.

Empirical model
A model to relate a response to one or more regressors or factors that is developed from data obtained from the system.

Ftest
Any test of signiicance involving the F distribution. The most common Ftests are (1) testing hypotheses about the variances or standard deviations of two independent normal distributions, (2) testing hypotheses about treatment means or variance components in the analysis of variance, and (3) testing signiicance of regression or tests on subsets of parameters in a regression model.

Fraction defective
In statistical quality control, that portion of a number of units or the output of a process that is defective.

Geometric mean.
The geometric mean of a set of n positive data values is the nth root of the product of the data values; that is, g x i n i n = ( ) = / w 1 1 .