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Solutions for Chapter 12.1: Inference for Linear Regression

Full solutions for The Practice of Statistics | 5th Edition

ISBN: 9781464108730

Solutions for Chapter 12.1: Inference for Linear Regression

Solutions for Chapter 12.1
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ISBN: 9781464108730

Chapter 12.1: Inference for Linear Regression includes 30 full step-by-step solutions. This expansive textbook survival guide covers the following chapters and their solutions. The Practice of Statistics was written by and is associated to the ISBN: 9781464108730. Since 30 problems in chapter 12.1: Inference for Linear Regression have been answered, more than 8987 students have viewed full step-by-step solutions from this chapter. This textbook survival guide was created for the textbook: The Practice of Statistics, edition: 5.

Key Statistics Terms and definitions covered in this textbook
• Additivity property of x 2

If two independent random variables X1 and X2 are distributed as chi-square with v1 and v2 degrees of freedom, respectively, Y = + X X 1 2 is a chi-square random variable with u = + v v 1 2 degrees of freedom. This generalizes to any number of independent chi-square random variables.

• All possible (subsets) regressions

A method of variable selection in regression that examines all possible subsets of the candidate regressor variables. Eficient computer algorithms have been developed for implementing all possible regressions

• 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.

• Asymptotic relative eficiency (ARE)

Used to compare hypothesis tests. The ARE of one test relative to another is the limiting ratio of the sample sizes necessary to obtain identical error probabilities for the two procedures.

• 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.

• Bimodal distribution.

A distribution with two modes

• Block

In experimental design, a group of experimental units or material that is relatively homogeneous. The purpose of dividing experimental units into blocks is to produce an experimental design wherein variability within blocks is smaller than variability between blocks. This allows the factors of interest to be compared in an environment that has less variability than in an unblocked experiment.

• Cause-and-effect diagram

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

• Cumulative distribution function

For a random variable X, the function of X deined as PX x ( ) ? that is used to specify the probability distribution.

• 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.

• Deining relation

A subset of effects in a fractional factorial design that deine the aliases in the design.

• Demingâ€™s 14 points.

A management philosophy promoted by W. Edwards Deming that emphasizes the importance of change and quality

• Dependent variable

The response variable in regression or a designed experiment.

• Discrete random variable

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

• Error of estimation

The difference between an estimated value and the true value.

• Exhaustive

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

• Expected value

The expected value of a random variable X is its long-term average or mean value. In the continuous case, the expected value of X is E X xf x dx ( ) = ?? ( ) ? ? where f ( ) x is the density function of the random variable X.

• Frequency distribution

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

• Generating function

A function that is used to determine properties of the probability distribution of a random variable. See Moment-generating function

• 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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