 Chapter 13.13.1: When an opinion poll calls residential telephonenumbers at random, ...
 Chapter 13.13.2: Logging in. At peak periods, 15% of attempted logins to an online ...
 Chapter 13.13.3: Computer instruction. A student studies binomial distributions usin...
 Chapter 13.13.4: I cant relax. Opinion polls find that 14% of Americans never have t...
 Chapter 13.13.5: Proofreading. Typing errors in a text are either nonword errors (as...
 Chapter 13.13.6: Random digit dialing. When an opinion poll calls residential teleph...
 Chapter 13.13.7: Tax returns. The Internal Revenue Service reports that 8.7% of indi...
 Chapter 13.13.8: Random digit dialing. (a) What is the mean number of calls that rea...
 Chapter 13.13.9: Proofreading. Return to the proofreading setting of Exercise 13.5.(...
 Chapter 13.13.10: Using Benfords law. According to Benfords law (Example 10.7, page 2...
 Chapter 13.13.11: Mark McGwires home runs. In 1998, Mark McGwire of the St. LouisCard...
 Chapter 13.13.12: Checking for survey errors. One way of checking the effect of under...
 Chapter 13.13.13: Joe reads that 1 out of 4 eggs contains salmonella bacteria. So he ...
 Chapter 13.13.14: In the previous exercise, the probability that at least one of Joes...
 Chapter 13.13.15: In a group of 10 college students, 4 are business majors. You choos...
 Chapter 13.13.16: If a basketball player makes 5 free throws and misses 2 free throws...
 Chapter 13.13.17: 13.17 A basketball player makes 70% of her free throws. She takes 7...
 Chapter 13.13.18: A basketball player makes 70% of her free throws. She takes 7 free ...
 Chapter 13.13.19: The probability of finding exactly 4 0s in a line 40 digits long is...
 Chapter 13.13.20: The mean number of 0s in a line 40 digits long is(a) 4. (b) 3.098. ...
 Chapter 13.13.21: Ten lines in the table contain 400 digits. The count of 0s in these...
 Chapter 13.13.22: Binomial setting? In each situation below, is it reasonable to use ...
 Chapter 13.13.23: Binomial setting? In which of these two sports settings is a binomi...
 Chapter 13.13.24: 50% of male Internet users in this age group visit an auction site ...
 Chapter 13.13.25: Testing ESP. In a test for ESP (extrasensory perception), a subject...
 Chapter 13.13.26: thinks that an index of stock prices has probability 0.65 of increa...
 Chapter 13.13.27: How many cars? Twenty percent of American households own three or m...
 Chapter 13.13.28: How many cars? Twenty percent of American households own three or m...
 Chapter 13.13.29: Reaching dropouts. High school dropouts make up 12.3% of all Americ...
 Chapter 13.13.30: Multiplechoice tests. Here is a simple probability model for multi...
 Chapter 13.13.31: Survey demographics. According to the Census Bureau, 12.4% of Ameri...
 Chapter 13.13.32: Leaking gas tanks. Leakage from underground gasoline tanks at servi...
 Chapter 13.13.33: Genetics. According to genetic theory, the blossom color in the sec...
 Chapter 13.13.34: Language study. Of American high school students, 41% are studying ...
 Chapter 13.13.35: percents into counts of the 500 students in the sample.)13.35 Is th...
 Chapter 13.13.36: Inspecting CDs. Example 13.5 concerns the count of bad CDs in inspe...
Solutions for Chapter Chapter 13: Binomial Distributions
Full solutions for The Basic Practice of Statistics  4th Edition
ISBN: 9780716774785
Solutions for Chapter Chapter 13: Binomial Distributions
Get Full SolutionsThe Basic Practice of Statistics was written by and is associated to the ISBN: 9780716774785. Since 36 problems in chapter Chapter 13: Binomial Distributions have been answered, more than 7697 students have viewed full stepbystep solutions from this chapter. This expansive textbook survival guide covers the following chapters and their solutions. This textbook survival guide was created for the textbook: The Basic Practice of Statistics, edition: 4. Chapter Chapter 13: Binomial Distributions includes 36 full stepbystep solutions.

2 k factorial experiment.
A full factorial experiment with k factors and all factors tested at only two levels (settings) each.

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.

Bimodal distribution.
A distribution with two modes

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 limit theorem
The simplest form of the central limit theorem states that the sum of n independently distributed random variables will tend to be normally distributed as n becomes large. It is a necessary and suficient condition that none of the variances of the individual random variables are large in comparison to their sum. There are more general forms of the central theorem that allow ininite variances and correlated random variables, and there is a multivariate version of the theorem.

Chisquare test
Any test of signiicance based on the chisquare distribution. The most common chisquare tests are (1) testing hypotheses about the variance or standard deviation of a normal distribution and (2) testing goodness of it of a theoretical distribution to sample data

Comparative experiment
An experiment in which the treatments (experimental conditions) that are to be studied are included in the experiment. The data from the experiment are used to evaluate the treatments.

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

Continuity correction.
A correction factor used to improve the approximation to binomial probabilities from a normal distribution.

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

Cook’s distance
In regression, Cook’s distance is a measure of the inluence of each individual observation on the estimates of the regression model parameters. It expresses the distance that the vector of model parameter estimates with the ith observation removed lies from the vector of model parameter estimates based on all observations. Large values of Cook’s distance indicate that the observation is inluential.

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 region
In hypothesis testing, this is the portion of the sample space of a test statistic that will lead to rejection of the null hypothesis.

Cumulative sum control chart (CUSUM)
A control chart in which the point plotted at time t is the sum of the measured deviations from target for all statistics up to time t

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.

Error variance
The variance of an error term or component in a model.

Exhaustive
A property of a collection of events that indicates that their union equals the sample space.

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 .