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# Solutions for Chapter 12-6: ASPECTS OF MULTIPLE REGRESSION MODELING

## Full solutions for Applied Statistics and Probability for Engineers | 3rd Edition

ISBN: 9780471204541

Solutions for Chapter 12-6: ASPECTS OF MULTIPLE REGRESSION MODELING

Solutions for Chapter 12-6
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##### ISBN: 9780471204541

This expansive textbook survival guide covers the following chapters and their solutions. Chapter 12-6: ASPECTS OF MULTIPLE REGRESSION MODELING includes 37 full step-by-step solutions. Since 37 problems in chapter 12-6: ASPECTS OF MULTIPLE REGRESSION MODELING have been answered, more than 21245 students have viewed full step-by-step solutions from this chapter. Applied Statistics and Probability for Engineers was written by and is associated to the ISBN: 9780471204541. This textbook survival guide was created for the textbook: Applied Statistics and Probability for Engineers , edition: 3.

Key Statistics Terms and definitions covered in this textbook
• Bayesâ€™ theorem

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

• Biased estimator

Unbiased estimator.

• Combination.

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

• Completely randomized design (or experiment)

A type of experimental design in which the treatments or design factors are assigned to the experimental units in a random manner. In designed experiments, a completely randomized design results from running all of the treatment combinations in random order.

• Conidence coeficient

The probability 1?a associated with a conidence interval expressing the probability that the stated interval will contain the true parameter value.

• Consistent estimator

An estimator that converges in probability to the true value of the estimated parameter as the sample size increases.

• Curvilinear regression

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

• 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.

• Density function

Another name for a probability density function

• Discrete random variable

A random variable with a inite (or countably ininite) range.

• Enumerative study

A study in which a sample from a population is used to make inference to the population. See Analytic study

• Erlang random variable

A continuous random variable that is the sum of a ixed number of independent, exponential random variables.

• 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 model-itting process and not on replication.

• F-test

Any test of signiicance involving the F distribution. The most common F-tests are (1) testing hypotheses about the variances or standard deviations of two independent normal distributions, (2) testing hypotheses about treatment means or variance components in the analysis of variance, and (3) testing signiicance of regression or tests on subsets of parameters in a regression model.

• False alarm

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

• Fraction defective control chart

See P chart

• Fractional factorial experiment

A type of factorial experiment in which not all possible treatment combinations are run. This is usually done to reduce the size of an experiment with several factors.

• Gamma function

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

• 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.

• Harmonic mean

The harmonic mean of a set of data values is the reciprocal of the arithmetic mean of the reciprocals of the data values; that is, h n x i n i = ? ? ? ? ? = ? ? 1 1 1 1 g .

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