 11.R11.1: Testing a genetic model Biologists wish to cross pairs of tobacco p...
 11.R11.2: Sorry, no chisquare We would prefer to learn from teachers who kno...
 11.R11.3: Stress and heart attacks You read a newspaper article that describe...
 11.R11.4: Sexy magazine ads? Researchers looked at 1509 fullpage ads that sh...
 11.R11.5: Popular kids Who were the popular kids at your elementary school? D...
 11.T11.1: Section I: Multiple Choice Select the best answer for each question...
 11.T11.2: Section I: Multiple Choice Select the best answer for each question...
 11.T11.3: Section I: Multiple Choice Select the best answer for each question...
 11.T11.4: Section I: Multiple Choice Select the best answer for each question...
 11.T11.5: Section I: Multiple Choice Select the best answer for each question...
 11.T11.6: Section I: Multiple Choice Select the best answer for each question...
 11.T11.7: Section I: Multiple Choice Select the best answer for each question...
 11.T11.8: Section I: Multiple Choice Select the best answer for each question...
 11.T11.9: Section I: Multiple Choice Select the best answer for each question...
 11.T11.10: Section I: Multiple Choice Select the best answer for each question...
 11.T11.11: Section II: Free Response Show all your work. Indicate clearly the ...
 11.T11.12: Section II: Free Response Show all your work. Indicate clearly the ...
 11.T11.13: Section II: Free Response Show all your work. Indicate clearly the ...
Solutions for Chapter 11: Inference for Ditribution of Categorical Data
Full solutions for The Practice of Statistics  5th Edition
ISBN: 9781464108730
Solutions for Chapter 11: Inference for Ditribution of Categorical Data
Get Full SolutionsChapter 11: Inference for Ditribution of Categorical Data includes 18 full stepbystep solutions. The Practice of Statistics was written by and is associated to the ISBN: 9781464108730. This textbook survival guide was created for the textbook: The Practice of Statistics, edition: 5. This expansive textbook survival guide covers the following chapters and their solutions. Since 18 problems in chapter 11: Inference for Ditribution of Categorical Data have been answered, more than 11141 students have viewed full stepbystep solutions from this chapter.

2 k factorial experiment.
A full factorial experiment with k factors and all factors tested at only two levels (settings) each.

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

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

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.

Average
See Arithmetic mean.

Average run length, or ARL
The average number of samples taken in a process monitoring or inspection scheme until the scheme signals that the process is operating at a level different from the level in which it began.

Backward elimination
A method of variable selection in regression that begins with all of the candidate regressor variables in the model and eliminates the insigniicant regressors one at a time until only signiicant regressors remain

Central composite design (CCD)
A secondorder response surface design in k variables consisting of a twolevel factorial, 2k axial runs, and one or more center points. The twolevel factorial portion of a CCD can be a fractional factorial design when k is large. The CCD is the most widely used design for itting a secondorder model.

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

Covariance matrix
A square matrix that contains the variances and covariances among a set of random variables, say, X1 , X X 2 k , , … . The main diagonal elements of the matrix are the variances of the random variables and the offdiagonal elements are the covariances between Xi and Xj . Also called the variancecovariance matrix. When the random variables are standardized to have unit variances, the covariance matrix becomes the correlation matrix.

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

Cumulative sum control chart (CUSUM)
A control chart in which the point plotted at time t is the sum of the measured deviations from target for all statistics up to time t

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

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

Distribution function
Another name for a cumulative distribution function.

Expected value
The expected value of a random variable X is its longterm 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.

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

Fixed factor (or fixed effect).
In analysis of variance, a factor or effect is considered ixed if all the levels of interest for that factor are included in the experiment. Conclusions are then valid about this set of levels only, although when the factor is quantitative, it is customary to it a model to the data for interpolating between these levels.

Generating function
A function that is used to determine properties of the probability distribution of a random variable. See Momentgenerating 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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