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# Solutions for Chapter 12: The Practice of Statistics 4th Edition

## Full solutions for The Practice of Statistics | 4th Edition

ISBN: 9781429245593

Solutions for Chapter 12

Solutions for Chapter 12
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##### ISBN: 9781429245593

This expansive textbook survival guide covers the following chapters and their solutions. Since 6 problems in chapter 12 have been answered, more than 33797 students have viewed full step-by-step solutions from this chapter. Chapter 12 includes 6 full step-by-step solutions. This textbook survival guide was created for the textbook: The Practice of Statistics, edition: 4. The Practice of Statistics was written by and is associated to the ISBN: 9781429245593.

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).

• Arithmetic mean

The arithmetic mean of a set of numbers x1 , x2 ,…, xn is their sum divided by the number of observations, or ( / )1 1 n xi t n ? = . The arithmetic mean is usually denoted by x , and is often called the average

• 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.

• Categorical data

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

• 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.

• 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.

• Components of variance

The individual components of the total variance that are attributable to speciic sources. This usually refers to the individual variance components arising from a random or mixed model analysis of variance.

• Conidence level

Another term for the conidence coeficient.

• Continuity correction.

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

• 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).

• Defects-per-unit control chart

See U chart

• Deming

W. Edwards Deming (1900–1993) was a leader in the use of statistical quality control.

• Discrete distribution

A probability distribution for a discrete random variable

• 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 variance

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

• 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.

• Experiment

A series of tests in which changes are made to the system under study

• Frequency distribution

An arrangement of the frequencies of observations in a sample or population according to the values that the observations take on

• Gamma function

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