 11.4.1: In Exercises 14, use the following listed chest deceleration measur...
 11.4.2: In Exercises 14, use the following listed chest deceleration measur...
 11.4.3: In Exercises 14, use the following listed chest deceleration measur...
 11.4.4: In Exercises 14, use the following listed chest deceleration measur...
 11.4.5: Lead and Verbal IQ Scores Example 1 used measured performance IQ sc...
 11.4.6: Lead and Full IQ Scores Example 1 used measured performance IQ scor...
 11.4.7: Highway Fuel Consumption Data Set 14 in Appendix B lists highway fu...
 11.4.8: City Fuel Consumption Data Set 14 in Appendix B lists city fuel con...
 11.4.9: Head Injury Crash Test Data Exercises 14 use chest deceleration dat...
 11.4.10: Pelvis Injury Crash Test Data Exercises 14 use chest deceleration d...
 11.4.11: Triathlon Times Jeff Parent is a statistics instructor who particip...
 11.4.12: Clancy, Rowling, Tolstoy Readability Pages were randomly selected b...
 11.4.13: Poplar Tree Weights Weights (kg) of poplar trees were obtained from...
 11.4.14: Poplar Tree Weights Weights (kg) of poplar trees were obtained from...
 11.4.15: In Exercises 15 and 16, use the data set from Appendix B. Nicotine ...
 11.4.16: In Exercises 15 and 16, use the data set from Appendix B. Secondhan...
 11.4.17: Tukey Test This section included a display of the Bonferroni test r...
 11.4.18: Bonferroni Test Shown below are partial results from using the Bonf...
Solutions for Chapter 11.4: Analysis of Variance
Full solutions for Essentials of Statistics  5th Edition
ISBN: 9780321924599
Solutions for Chapter 11.4: Analysis of Variance
Get Full SolutionsSince 18 problems in chapter 11.4: Analysis of Variance have been answered, more than 14507 students have viewed full stepbystep solutions from this chapter. Essentials of Statistics was written by and is associated to the ISBN: 9780321924599. Chapter 11.4: Analysis of Variance includes 18 full stepbystep solutions. This textbook survival guide was created for the textbook: Essentials of Statistics, edition: 5. This expansive textbook survival guide covers the following chapters and their solutions.

Adjusted R 2
A variation of the R 2 statistic that compensates for the number of parameters in a regression model. Essentially, the adjustment is a penalty for increasing the number of parameters in the model. Alias. In a fractional factorial experiment when certain factor effects cannot be estimated uniquely, they are said to be aliased.

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.

Bayes’ 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.

Bivariate distribution
The joint probability distribution of two random variables.

Box plot (or box and whisker plot)
A graphical display of data in which the box contains the middle 50% of the data (the interquartile range) with the median dividing it, and the whiskers extend to the smallest and largest values (or some deined lower and upper limits).

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

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

Components of variance
The individual components of the total variance that are attributable to speciic sources. This usually refers to the individual variance components arising from a random or mixed model analysis of variance.

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

Convolution
A method to derive the probability density function of the sum of two independent random variables from an integral (or sum) of probability density (or mass) functions.

Cook’s distance
In regression, Cook’s distance is a measure of the inluence of each individual observation on the estimates of the regression model parameters. It expresses the distance that the vector of model parameter estimates with the ith observation removed lies from the vector of model parameter estimates based on all observations. Large values of Cook’s distance indicate that the observation is inluential.

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.

Defect concentration diagram
A quality tool that graphically shows the location of defects on a part or in a process.

Designed experiment
An experiment in which the tests are planned in advance and the plans usually incorporate statistical models. See Experiment

Discrete distribution
A probability distribution for a discrete random variable

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.

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

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

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 .