- Chapter 1:
- Chapter 10: One- and Two-Sample Tests of Hypotheses
- Chapter 11: Simple Linear Regression and Correlation
- Chapter 12: Multiple Linear Regression and Certain Nonlinear Regression Models
- Chapter 13: One-Factor Experiments: General
- Chapter 14: Factorial Experiments (Two or More Factors)
- Chapter 15: 2k Factorial Experiments and Fractions
- Chapter 16: Nonparametric Statistics
- Chapter 17: Statistical Quality Control
- Chapter 18: Bayesian Statistics
- Chapter 2:
- Chapter 3:
- Chapter 4:
- Chapter 5:
- Chapter 6:
- Chapter 7: Functions of Random Variables (Optional)
- Chapter 8: Fundamental Sampling Distributions and Data Descriptions
- Chapter 9:
Probability and Statistics for Engineers and the Scientists 9th Edition - Solutions by Chapter
Full solutions for Probability and Statistics for Engineers and the Scientists | 9th Edition
Probability and Statistics for Engineers and the Scientists | 9th Edition - Solutions by ChapterGet Full Solutions
In hypothesis testing, a region in the sample space of the test statistic such that if the test statistic falls within it, the null hypothesis cannot be rejected. This terminology is used because rejection of H0 is always a strong conclusion and acceptance of H0 is generally a weak conclusion
A qualitative characteristic of an item or unit, usually arising in quality control. For example, classifying production units as defective or nondefective results in attributes data.
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).
Data consisting of counts or observations that can be classiied into categories. The categories may be descriptive.
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.
A tabular arrangement expressing the assignment of members of a data set according to two or more categories or classiication criteria
A probability distribution for a continuous random variable.
Another name for a probability density function
An experiment in which the tests are planned in advance and the plans usually incorporate statistical models. See Experiment
A model to relate a response to one or more regressors or factors that is developed from data obtained from the system.
Estimate (or point estimate)
The numerical value of a point estimator.
Exponential random variable
A series of tests in which changes are made to the system under study
A signal from a control chart when no assignable causes are present
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
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.
Another name for the normal distribution, based on the strong connection of Karl F. Gauss to the normal distribution; often used in physics and electrical engineering applications
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.
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 .
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 .