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The Practice of Statistics 4th Edition  Solutions by Chapter
Full solutions for The Practice of Statistics  4th Edition
ISBN: 9781429245593
The Practice of Statistics  4th Edition  Solutions by Chapter
Get Full SolutionsSince problems from 12 chapters in The Practice of Statistics have been answered, more than 3108 students have viewed full stepbystep answer. The full stepbystep solution to problem in The Practice of Statistics were answered by Sieva Kozinsky, our top Statistics solution expert on 09/04/17, 10:29PM. The Practice of Statistics was written by Sieva Kozinsky and is associated to the ISBN: 9781429245593. This textbook survival guide was created for the textbook: The Practice of Statistics, edition: 4. This expansive textbook survival guide covers the following chapters: 12.

2 k p  factorial experiment
A fractional factorial experiment with k factors tested in a 2 ? p fraction with all factors tested at only two levels (settings) each

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

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

Bias
An effect that systematically distorts a statistical result or estimate, preventing it from representing the true quantity of interest.

Coeficient of determination
See R 2 .

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

Conditional probability
The probability of an event given that the random experiment produces an outcome in another event.

Conditional probability distribution
The distribution of a random variable given that the random experiment produces an outcome in an event. The given event might specify values for one or more other random variables

Conditional probability mass function
The probability mass function of the conditional probability distribution of a discrete random variable.

Contrast
A linear function of treatment means with coeficients that total zero. A contrast is a summary of treatment means that is of interest in an experiment.

Curvilinear regression
An expression sometimes used for nonlinear regression models or polynomial regression models.

Density function
Another name for a probability density function

Error propagation
An analysis of how the variance of the random variable that represents that output of a system depends on the variances of the inputs. A formula exists when the output is a linear function of the inputs and the formula is simpliied if the inputs are assumed to be independent.

Error sum of squares
In analysis of variance, this is the portion of total variability that is due to the random component in the data. It is usually based on replication of observations at certain treatment combinations in the experiment. It is sometimes called the residual sum of squares, although this is really a better term to use only when the sum of squares is based on the remnants of a modelitting process and not on replication.

False alarm
A signal from a control chart when no assignable causes are present

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

Firstorder model
A model that contains only irstorder terms. For example, the irstorder response surface model in two variables is y xx = + ?? ? ? 0 11 2 2 + + . A irstorder model is also called a main effects model

Gaussian distribution
Another name for the normal distribution, based on the strong connection of Karl F. Gauss to the normal distribution; often used in physics and electrical engineering applications

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.
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