- Chapter 1: Overview and Descriptive Statistics
- Chapter 10: The Analysis of Variance
- Chapter 11: Multifactor of Analysis of Variance
- Chapter 12: Simple Linear Regression and Correlation
- Chapter 13: Nonlinear and Mutiple Regression
- Chapter 14: Goodness-of-Fit Tests and Categorial Data Analysis
- Chapter 15: Distribution-Free Procedures
- Chapter 16: Quality Control Methods
- Chapter 2: Probability
- Chapter 3: Discrete Random Variables and Probability Distributions
- Chapter 4: Continuous Random Variables and Probability Distributions
- Chapter 5: Joint Probability Distributions and Random Samples
- Chapter 6: Point Estimation
- Chapter 7: Statistical Intervals Based on a Single Sample
- Chapter 8: Tests on Hypotheses Based on a Single Sample
- Chapter 9: Inferences Based on Two Samples
- Chapter SE1: Sample Exams
- Chapter SE2: Sample Exams
- Chapter SE3: Sample Exams
- Chapter SE4: Sample Exams
- Chapter SE5: Sample Exams
- Chapter SE6: Sample Exams
- Chapter SE7: Sample Exams
Probability and Statistics for Engineering and the Sciences (with Student Suite Online) 7th Edition - Solutions by Chapter
Full solutions for Probability and Statistics for Engineering and the Sciences (with Student Suite Online) | 7th Edition
Probability and Statistics for Engineering and the Sciences (with Student Suite Online) | 7th Edition - Solutions by ChapterGet Full Solutions
2 k p - factorial experiment
A fractional factorial experiment with k factors tested in a 2 ? p fraction with all factors tested at only two levels (settings) each
A formula used to determine the probability of the union of two (or more) events from the probabilities of the events and their intersection(s).
Analysis of variance (ANOVA)
A method of decomposing the total variability in a set of observations, as measured by the sum of the squares of these observations from their average, into component sums of squares that are associated with speciic deined sources of variation
The portion of the variability in a set of observations that is due to only random forces and which cannot be traced to speciic sources, such as operators, materials, or equipment. Also called a common cause.
The variance of the conditional probability distribution of a random variable.
A two-dimensional graphic used for a bivariate probability density function that displays curves for which the probability density function is constant.
Formulas used to determine the number of elements in sample spaces and events.
In hypothesis testing, this is the portion of the sample space of a test statistic that will lead to rejection of the null hypothesis.
Another name for factors that are arranged in a factorial experiment.
Cumulative distribution function
For a random variable X, the function of X deined as PX x ( ) ? that is used to specify the probability distribution.
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.
Another name for a probability density function
A matrix that provides the tests that are to be conducted in an experiment.
An experiment in which the tests are planned in advance and the plans usually incorporate statistical models. See Experiment
A probability distribution for a discrete random variable
Error mean square
The error sum of squares divided by its number of degrees of freedom.
A property of a collection of events that indicates that their union equals the sample space.
A type of experimental design in which every level of one factor is tested in combination with every level of another factor. In general, in a factorial experiment, all possible combinations of factor levels are tested.
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
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 .
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