 15.6.32: Three treatments were compared using acompletely randomized design....
 15.6.33: Four treatments were compared using acompletely randomized design. ...
 15.6.34: Swampy Sites II Exercise 11.13 presentsdata (see data set EX1113) o...
 15.6.35: Heart Rate and Exercise Exercise 11.60presented data (data set EX11...
 15.6.36: pH Levels in Water A sampling ofthe acidity of rain for 10 randomly...
 15.6.37: Advertising Campaigns The resultsof an experiment to investigate pr...
Solutions for Chapter 15.6: The KruskalWallis HTest for Completely Randomized Designs
Full solutions for Introduction to Probability and Statistics 1  14th Edition
ISBN: 9781133103752
Solutions for Chapter 15.6: The KruskalWallis HTest for Completely Randomized Designs
Get Full SolutionsChapter 15.6: The KruskalWallis HTest for Completely Randomized Designs includes 6 full stepbystep solutions. This textbook survival guide was created for the textbook: Introduction to Probability and Statistics 1, edition: 14. Since 6 problems in chapter 15.6: The KruskalWallis HTest for Completely Randomized Designs have been answered, more than 9324 students have viewed full stepbystep solutions from this chapter. This expansive textbook survival guide covers the following chapters and their solutions. Introduction to Probability and Statistics 1 was written by and is associated to the ISBN: 9781133103752.

2 k factorial experiment.
A full factorial experiment with k factors and 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).

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

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.

Chisquare test
Any test of signiicance based on the chisquare distribution. The most common chisquare tests are (1) testing hypotheses about the variance or standard deviation of a normal distribution and (2) testing goodness of it of a theoretical distribution to sample data

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.

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

Conditional probability density function
The probability density function of the conditional probability distribution of a continuous random variable.

Conditional variance.
The variance of the conditional probability distribution of a random variable.

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

Contingency table.
A tabular arrangement expressing the assignment of members of a data set according to two or more categories or classiication criteria

Continuous distribution
A probability distribution for a continuous random variable.

Correlation matrix
A square matrix that contains the correlations among a set of random variables, say, XX X 1 2 k , ,…, . The main diagonal elements of the matrix are unity and the offdiagonal elements rij are the correlations between Xi and Xj .

Dispersion
The amount of variability exhibited by data

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.

Extra sum of squares method
A method used in regression analysis to conduct a hypothesis test for the additional contribution of one or more variables to a model.

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

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 .

Geometric random variable
A discrete random variable that is the number of Bernoulli trials until a success occurs.

Hat matrix.
In multiple regression, the matrix H XXX X = ( ) ? ? 1 . This a projection matrix that maps the vector of observed response values into a vector of itted values by yˆ = = X X X X y Hy ( ) ? ? ?1 .