- Chapter 1: Introduction to Statistics
- Chapter 10: Analysis of Variance
- Chapter 11: Goodness of Fit Tests and Categorical Data Analysis
- Chapter 12: Nonparametric Hypothesis Tests
- Chapter 13: Quality Control
- Chapter 14: Life Testing
- Chapter 15: Simulation, Bootstrap Statistical Methods, and Permutation Tests
- Chapter 2: Descriptive Statistics
- Chapter 3: Elements of Probability
- Chapter 4: Random Variables and Expectation
- Chapter 5: Special Random Variables
- Chapter 6: Distributions of Sampling Statistics
- Chapter 7: Parameter Estimation
- Chapter 8: Hypothesis Testing
- Chapter 9: Regression
Introduction to Probability and Statistics for Engineers and Scientists 5th Edition - Solutions by Chapter
Full solutions for Introduction to Probability and Statistics for Engineers and Scientists | 5th Edition
Introduction to Probability and Statistics for Engineers and Scientists | 5th Edition - Solutions by ChapterGet Full Solutions
In statistical hypothesis testing, this is a hypothesis other than the one that is being tested. The alternative hypothesis contains feasible conditions, whereas the null hypothesis speciies conditions that are under test
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.
Attribute control chart
Any control chart for a discrete random variable. See Variables control chart.
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.
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.
Coeficient of determination
See R 2 .
A subset selected without replacement from a set used to determine the number of outcomes in events and sample spaces.
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.
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.
The probability of an event given that the random experiment produces an outcome in another event.
See Control chart.
Cumulative normal distribution function
The cumulative distribution of the standard normal distribution, often denoted as ?( ) x and tabulated in Appendix Table II.
Defect concentration diagram
A quality tool that graphically shows the location of defects on a part or in a process.
Discrete random variable
A random variable with a inite (or countably ininite) range.
Estimate (or point estimate)
The numerical value of a point estimator.
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 model that contains only irstorder terms. For example, the irst-order response surface model in two variables is y xx = + ?? ? ? 0 11 2 2 + + . A irst-order model is also called a main effects model
Fractional factorial experiment
A type of factorial experiment in which not all possible treatment combinations are run. This is usually done to reduce the size of an experiment with several factors.
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 .