- Chapter 1:
- Chapter 1: What Is Statistics?
- Chapter 10:
- Chapter 10: Hypothesis Testing
- Chapter 11:
- Chapter 11: Linear Models and Estimation by Least Squares
- Chapter 12:
- Chapter 12: Considerations in Designing Experiments
- Chapter 13:
- Chapter 13: The Analysis of Variance
- Chapter 14:
- Chapter 14: Analysis of Categorical Data
- Chapter 15:
- Chapter 15: Nonparametric Statistics
- Chapter 16:
- Chapter 16: Introduction to Bayesian Methods for Inference
- Chapter 2:
- Chapter 2: Probability
- Chapter 3:
- Chapter 3: Discrete Random Variables and Their Probability Distributions
- Chapter 4:
- Chapter 4: Continuous Variables and Their Probability Distributions
- Chapter 5:
- Chapter 5: Multivariate Probability Distributions
- Chapter 6:
- Chapter 6: Functions of Random Variables
- Chapter 7:
- Chapter 7: Sampling Distributions and the Central Limit Theorem
- Chapter 8:
- Chapter 8: Estimation
- Chapter 9:
- Chapter 9: Properties of Point Estimators and Methods of Estimation
Mathematical Statistics with Applications 7th Edition - Solutions by Chapter
Full solutions for Mathematical Statistics with Applications | 7th Edition
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.
Binomial random variable
A discrete random variable that equals the number of successes in a ixed number of Bernoulli trials.
Data consisting of counts or observations that can be classiied into categories. The categories may be descriptive.
An experiment in which the treatments (experimental conditions) that are to be studied are included in the experiment. The data from the experiment are used to evaluate the treatments.
A linear function of treatment means with coeficients that total zero. A contrast is a summary of treatment means that is of interest in an experiment.
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.
A dimensionless measure of the linear association between two variables, usually lying in the interval from ?1 to +1, with zero indicating the absence of correlation (but not necessarily the independence of the two variables).
A measure of association between two random variables obtained as the expected value of the product of the two random variables around their means; that is, Cov(X Y, ) [( )( )] =? ? E X Y ? ? X Y .
In hypothesis testing, this is the portion of the sample space of a test statistic that will lead to rejection of the null hypothesis.
Defect concentration diagram
A quality tool that graphically shows the location of defects on a part or in a process.
A subset of effects in a fractional factorial design that deine the aliases in the design.
Deming’s 14 points.
A management philosophy promoted by W. Edwards Deming that emphasizes the importance of change and quality
A probability distribution for a discrete random variable
Erlang random variable
A continuous random variable that is the sum of a ixed number of independent, exponential random variables.
Error mean square
The error sum of squares divided by its number of degrees of freedom.
A subset of a sample space.
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.
A signal from a control chart when no assignable causes are present
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.
Geometric random variable
A discrete random variable that is the number of Bernoulli trials until a success occurs.
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