 Chapter 20.20.1: Do college students pray? A study of religious practices among coll...
 Chapter 20.20.2: Playing games online. A random sample of 1100 teenagers (ages 12 to...
 Chapter 20.20.3: Student drinking. The College Alcohol Study interviewed an SRS of 1...
 Chapter 20.20.4: Students on diets. A sample survey interviews an SRS of 267 college...
 Chapter 20.20.5: Student drinking, continued. Suppose that half of all college stude...
 Chapter 20.20.6: No inference. A local television station conducts a callin poll ab...
 Chapter 20.20.7: No confidence interval. In the National AIDS Behavioral Surveys sam...
 Chapter 20.20.8: How common is SAT coaching? A random sample of students who took th...
 Chapter 20.20.9: Deaths from guns. The Harris Poll asked a random sample of 1009 adu...
 Chapter 20.20.10: Drugdetecting rats? Dogs are big and expensive. Rats are small and...
 Chapter 20.20.11: Whelks and mussels. Sample surveys usually contact large samples, s...
 Chapter 20.20.12: Highrisk behavior. In the National AIDS Behavioral Surveys sample ...
 Chapter 20.20.13: Canadians and doctorassisted suicide. A Gallup Poll asked a sample...
 Chapter 20.20.14: Can you taste PTC? PTC is a substance that has a strong bitter tast...
 Chapter 20.20.15: Spinning pennies. Spinning a coin, unlike tossing it, may not give ...
 Chapter 20.20.16: Vote for the best face? We often judge other people by their faces....
 Chapter 20.20.17: No test. Explain why we cant use the z test for a proportion in the...
 Chapter 20.20.18: Sports Illustrated asked a random sample of 757 Division I college ...
 Chapter 20.20.19: The standard deviation of the distribution of p in the previous exe...
 Chapter 20.20.20: In fact, 273 of the 757 athletes in the Sports Illustrated sample s...
 Chapter 20.20.21: Based on the Sports Illustrated sample, the 95% largesample confid...
 Chapter 20.20.22: How many athletes must be interviewed to estimate the proportion co...
 Chapter 20.20.23: An opinion poll asks an SRS of 100 college seniors how they view th...
 Chapter 20.20.24: The sample survey in Exercise 20.23 actually called 130 seniors, bu...
 Chapter 20.20.25: Does the poll in Exercise 20.23 give reason to conclude that more t...
 Chapter 20.20.26: The value of the z statistic for the test of the previous exercise ...
 Chapter 20.20.27: "A Harris Poll found that 54% of American adults do not think that ...
 Chapter 20.20.28: Reporting cheating. Students are reluctant to report cheating by ot...
 Chapter 20.20.29: Do college students pray? Social scientists asked 127 undergraduate...
 Chapter 20.20.30: Which font? Plain type fonts such as Times New Roman are easier to ...
 Chapter 20.20.31: Seat belt use. The proportion of drivers who use seat belts depends...
 Chapter 20.20.32: Running red lights. A random digit dialing telephone survey of 880 ...
 Chapter 20.20.33: Seat belt use, continued. Do the data in Exercise 20.31 give good r...
 Chapter 20.20.34: Seat belt use: planning a study. How large a sample would be needed...
 Chapter 20.20.35: Detecting genetically modified soybeans. Most soybeans grown in the...
 Chapter 20.20.36: The IRS plans an SRS. The Internal Revenue Service plans to examine...
 Chapter 20.20.37: Smallbusiness failures. A study of the survival of small businesse...
 Chapter 20.20.38: Customer satisfaction. An automobile manufacturer would like to kno...
 Chapter 20.20.39: Surveying students. You are planning a survey of students at a larg...
 Chapter 20.20.40: Student drinking. The College Alcohol Study interviewed a sample of...
 Chapter 20.20.41: Condom usage. The National AIDS Behavioral Surveys (Example 20.1) a...
 Chapter 20.20.42: Online publishing. Publishing scientific papers online is fast, and...
 Chapter 20.20.43: More online publishing. The previous exercise describes a survey of...
Solutions for Chapter Chapter 20: Inference about a Population Proportion
Full solutions for The Basic Practice of Statistics  4th Edition
ISBN: 9780716774785
Solutions for Chapter Chapter 20: Inference about a Population Proportion
Get Full SolutionsThe Basic Practice of Statistics was written by and is associated to the ISBN: 9780716774785. Since 43 problems in chapter Chapter 20: Inference about a Population Proportion have been answered, more than 7732 students have viewed full stepbystep solutions from this chapter. This textbook survival guide was created for the textbook: The Basic Practice of Statistics, edition: 4. Chapter Chapter 20: Inference about a Population Proportion includes 43 full stepbystep solutions. This expansive textbook survival guide covers the following chapters and their solutions.

Alternative hypothesis
In statistical hypothesis testing, this is a hypothesis other than the one that is being tested. The alternative hypothesis contains feasible conditions, whereas the null hypothesis speciies conditions that are under test

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.

Bivariate normal distribution
The joint distribution of two normal random variables

C chart
An attribute control chart that plots the total number of defects per unit in a subgroup. Similar to a defectsperunit or U chart.

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.

Chisquare (or chisquared) random variable
A continuous random variable that results from the sum of squares of independent standard normal random variables. It is a special case of a gamma random variable.

Combination.
A subset selected without replacement from a set used to determine the number of outcomes in events and sample spaces.

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

Contrast
A linear function of treatment means with coeficients that total zero. A contrast is a summary of treatment means that is of interest in an experiment.

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

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.

Eficiency
A concept in parameter estimation that uses the variances of different estimators; essentially, an estimator is more eficient than another estimator if it has smaller variance. When estimators are biased, the concept requires modiication.

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

Enumerative study
A study in which a sample from a population is used to make inference to the population. See Analytic study

Fixed factor (or fixed effect).
In analysis of variance, a factor or effect is considered ixed if all the levels of interest for that factor are included in the experiment. Conclusions are then valid about this set of levels only, although when the factor is quantitative, it is customary to it a model to the data for interpolating between these levels.

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 .

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 .

Hat matrix.
In multiple regression, the matrix H XXX X = ( ) ? ? 1 . This a projection matrix that maps the vector of observed response values into a vector of itted values by yˆ = = X X X X y Hy ( ) ? ? ?1 .