- 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
`-error (or `-risk)
In hypothesis testing, an error incurred by rejecting a null hypothesis when it is actually true (also called a type I error).
All possible (subsets) regressions
A method of variable selection in regression that examines all possible subsets of the candidate regressor variables. Eficient computer algorithms have been developed for implementing all possible regressions
Asymptotic relative eficiency (ARE)
Used to compare hypothesis tests. The ARE of one test relative to another is the limiting ratio of the sample sizes necessary to obtain identical error probabilities for the two procedures.
A method of variable selection in regression that begins with all of the candidate regressor variables in the model and eliminates the insigniicant regressors one at a time until only signiicant regressors remain
An equation for a conditional probability such as PA B ( | ) in terms of the reverse conditional probability PB A ( | ).
The tendency of data to cluster around some value. Central tendency is usually expressed by a measure of location such as the mean, median, or mode.
A two-dimensional graphic used for a bivariate probability density function that displays curves for which the probability density function is constant.
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 square matrix that contains the correlations among a set of random variables, say, XX X 1 2 k , ,…, . The main diagonal elements of the matrix are unity and the off-diagonal elements rij are the correlations between Xi and Xj .
W. Edwards Deming (1900–1993) was a leader in the use of statistical quality control.
A matrix that provides the tests that are to be conducted in an experiment.
Erlang random variable
A continuous random variable that is the sum of a ixed number of independent, exponential random variables.
Error sum of squares
In analysis of variance, this is the portion of total variability that is due to the random component in the data. It is usually based on replication of observations at certain treatment combinations in the experiment. It is sometimes called the residual sum of squares, although this is really a better term to use only when the sum of squares is based on the remnants of a model-itting process and not on replication.
The variance of an error term or component in a model.
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.
The distribution of the random variable deined as the ratio of two independent chi-square random variables, each divided by its number of degrees of freedom.
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.
In statistical quality control, that portion of a number of units or the output of a process that is defective.
An arrangement of the frequencies of observations in a sample or population according to the values that the observations take on
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 .
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