 4.4.1BSC: Notation Let R be the event of randomly selecting a senator and get...
 4.4.2BSC: Independent and Dependent Events Are events R and D from Exercise i...
 4.4.3BSC: Independent and Dependent Events True or false: The event of findin...
 4.4.4BSC: Sample for a Poll There are currently 28,741,346 adults in Californ...
 4.4.5BSC: Independent and Dependent Events. In Exercise , (a) determine wheth...
 4.4.6BSC: Independent and Dependent Events. In Exercise , (a) determine wheth...
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 4.4.13BSC: PreEmployment Drug Screening. In Exercise, use the test results su...
 4.4.14BSC: PreEmployment Drug Screening. In Exercise, use the test results su...
 4.4.15BSC: PreEmployment Drug Screening. In Exercise, use the test results su...
 4.4.16BSC: PreEmployment Drug Screening. In Exercise, use the test results su...
 4.4.17BSC: Acceptance Sampling With one method of a procedure called acceptanc...
 4.4.18BSC: Acceptance Sampling With one method of a procedure called acceptanc...
 4.4.19BSC: Redundancy in Computer Hard Drives It is generally recognized that ...
 4.4.20BSC: Redundancy in Aircraft Radios The FAA requires that commercial airc...
 4.4.21BSC: Born on the 4th of July For the following, ignore leap years and as...
 4.4.22BSC: Hiring Employees Assume that Google, Inc. hires employees on the di...
 4.4.23BSC: In Exercise, use these results from the “1PanelTHC” test for mari...
 4.4.24BSC: In Exercise, use these results from the “1PanelTHC” test for mari...
 4.4.25BSC: In Exercise, use these results from the “1PanelTHC” test for mari...
 4.4.26BSC: In Exercise, use these results from the “1PanelTHC” test for mari...
 4.4.27BSC: In Exercise, find the probabilities and indicate when the “5% guide...
 4.4.28BSC: In Exercise, find the probabilities and indicate when the “5% guide...
 4.4.29BSC: In Exercise, find the probabilities and indicate when the “5% guide...
 4.4.30BSC: In Exercise, find the probabilities and indicate when the “5% guide...
 4.4.31BB: System Reliability Refer to the figure at the top of the next page ...
 4.4.32BB: Same Birthdays If 25 people are randomly selected, find the probabi...
Solutions for Chapter 4.4: Elementary Statistics 12th Edition
Full solutions for Elementary Statistics  12th Edition
ISBN: 9780321836960
Solutions for Chapter 4.4
Get Full SolutionsElementary Statistics was written by and is associated to the ISBN: 9780321836960. Chapter 4.4 includes 32 full stepbystep solutions. This textbook survival guide was created for the textbook: Elementary Statistics, edition: 12. Since 32 problems in chapter 4.4 have been answered, more than 171072 students have viewed full stepbystep solutions from this chapter. This expansive textbook survival guide covers the following chapters and their solutions.

Analytic study
A study in which a sample from a population is used to make inference to a future population. Stability needs to be assumed. See Enumerative study

Assignable cause
The portion of the variability in a set of observations that can be traced to speciic causes, such as operators, materials, or equipment. Also called a special cause.

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.

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.

Confounding
When a factorial experiment is run in blocks and the blocks are too small to contain a complete replicate of the experiment, one can run a fraction of the replicate in each block, but this results in losing information on some effects. These effects are linked with or confounded with the blocks. In general, when two factors are varied such that their individual effects cannot be determined separately, their effects are said to be confounded.

Counting techniques
Formulas used to determine the number of elements in sample spaces and events.

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 .

Covariance matrix
A square matrix that contains the variances and covariances among a set of random variables, say, X1 , X X 2 k , , … . The main diagonal elements of the matrix are the variances of the random variables and the offdiagonal elements are the covariances between Xi and Xj . Also called the variancecovariance matrix. When the random variables are standardized to have unit variances, the covariance matrix becomes the correlation matrix.

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.

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.

Cumulative distribution function
For a random variable X, the function of X deined as PX x ( ) ? that is used to specify the probability distribution.

Distribution free method(s)
Any method of inference (hypothesis testing or conidence interval construction) that does not depend on the form of the underlying distribution of the observations. Sometimes called nonparametric method(s).

Error of estimation
The difference between an estimated value and the true value.

Estimator (or point estimator)
A procedure for producing an estimate of a parameter of interest. An estimator is usually a function of only sample data values, and when these data values are available, it results in an estimate of the parameter of interest.

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

Fraction defective control chart
See P chart

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 .

Goodness of fit
In general, the agreement of a set of observed values and a set of theoretical values that depend on some hypothesis. The term is often used in itting a theoretical distribution to a set of observations.