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Textbooks / Statistics / Statistical Techniques in Business and Economics 15

# Statistical Techniques in Business and Economics 15th Edition - Solutions by Chapter

## Full solutions for Statistical Techniques in Business and Economics | 15th Edition

ISBN: 9780073401805

Statistical Techniques in Business and Economics | 15th Edition - Solutions by Chapter

Solutions by Chapter
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##### ISBN: 9780073401805

The full step-by-step solution to problem in Statistical Techniques in Business and Economics were answered by , our top Statistics solution expert on 03/16/18, 04:51PM. Statistical Techniques in Business and Economics was written by and is associated to the ISBN: 9780073401805. Since problems from 20 chapters in Statistical Techniques in Business and Economics have been answered, more than 57227 students have viewed full step-by-step answer. This expansive textbook survival guide covers the following chapters: 20. This textbook survival guide was created for the textbook: Statistical Techniques in Business and Economics, edition: 15.

Key Statistics Terms and definitions covered in this textbook
• a-error (or a-risk)

In hypothesis testing, an error incurred by failing to reject a null hypothesis when it is actually false (also called a type II error).

• 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

• 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.

• Axioms of probability

A set of rules that probabilities deined on a sample space must follow. See Probability

• Binomial random variable

A discrete random variable that equals the number of successes in a ixed number of Bernoulli trials.

• Bivariate distribution

The joint probability distribution of two random variables.

• Combination.

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

• 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.

• Conditional mean

The mean of the conditional probability distribution of a random variable.

• Continuous distribution

A probability distribution for a continuous random variable.

• Continuous uniform random variable

A continuous random variable with range of a inite interval and a constant probability density function.

• Correlation coeficient

A dimensionless measure of the linear association between two variables, usually lying in the interval from ?1 to +1, with zero indicating the absence of correlation (but not necessarily the independence of the two variables).

• Defect

Used in statistical quality control, a defect is a particular type of nonconformance to speciications or requirements. Sometimes defects are classiied into types, such as appearance defects and functional defects.

• 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.

• Designed experiment

An experiment in which the tests are planned in advance and the plans usually incorporate statistical models. See Experiment

• Distribution function

Another name for a cumulative distribution function.

• 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.

• Gamma function

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

• 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 .