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Solutions for Chapter 6.7: Normal as Approximation to Binomial

Full solutions for Essentials of Statistics | 5th Edition

ISBN: 9780321924599

Solutions for Chapter 6.7: Normal as Approximation to Binomial

Solutions for Chapter 6.7
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ISBN: 9780321924599

Since 26 problems in chapter 6.7: Normal as Approximation to Binomial have been answered, more than 14510 students have viewed full step-by-step solutions from this chapter. This expansive textbook survival guide covers the following chapters and their solutions. Chapter 6.7: Normal as Approximation to Binomial includes 26 full step-by-step solutions. Essentials of Statistics was written by and is associated to the ISBN: 9780321924599. This textbook survival guide was created for the textbook: Essentials of Statistics, edition: 5.

Key Statistics Terms and definitions covered in this textbook
• Analysis of variance (ANOVA)

A method of decomposing the total variability in a set of observations, as measured by the sum of the squares of these observations from their average, into component sums of squares that are associated with speciic deined sources of variation

• Average

See Arithmetic mean.

• Bayesâ€™ 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.

• Bayesâ€™ theorem

An equation for a conditional probability such as PA B ( | ) in terms of the reverse conditional probability PB A ( | ).

• Categorical data

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

• Causal variable

When y fx = ( ) and y is considered to be caused by x, x is sometimes called a causal variable

• Cause-and-effect diagram

A chart used to organize the various potential causes of a problem. Also called a ishbone diagram.

• 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

• Correction factor

A term used for the quantity ( / )( ) 1 1 2 n xi i n ? = that is subtracted from xi i n 2 ? =1 to give the corrected sum of squares deined as (/ ) ( ) 1 1 2 n xx i x i n ? = i ? . The correction factor can also be written as nx 2 .

• 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.

• 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).

• 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.

• Error variance

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

• Estimate (or point estimate)

The numerical value of a point estimator.

• Exhaustive

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

• Gamma function

A function used in the probability density function of a gamma random variable that can be considered to extend factorials

• Gamma random variable

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

• 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.

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