- 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
Adjusted R 2
A variation of the R 2 statistic that compensates for the number of parameters in a regression model. Essentially, the adjustment is a penalty for increasing the number of parameters in the model. Alias. In a fractional factorial experiment when certain factor effects cannot be estimated uniquely, they are said to be aliased.
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.
An equation for a conditional probability such as PA B ( | ) in terms of the reverse conditional probability PB A ( | ).
Bivariate normal distribution
The joint distribution of two normal random variables
Conditional probability density function
The probability density function of the conditional probability distribution of a continuous random variable.
Conditional probability distribution
The distribution of a random variable given that the random experiment produces an outcome in an event. The given event might specify values for one or more other random variables
A tabular arrangement expressing the assignment of members of a data set according to two or more categories or classiication criteria
In regression, Cook’s distance is a measure of the inluence of each individual observation on the estimates of the regression model parameters. It expresses the distance that the vector of model parameter estimates with the ith observation removed lies from the vector of model parameter estimates based on all observations. Large values of Cook’s distance indicate that the observation is inluential.
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 .
Defects-per-unit control chart
See U chart
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.
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
Fisher’s least signiicant difference (LSD) method
A series of pair-wise hypothesis tests of treatment means in an experiment to determine which means differ.
Gamma random variable
A random variable that generalizes an Erlang random variable to noninteger values of the parameter r
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 .
Goodness of fit
In general, the agreement of a set of observed values and a set of theoretical values that depend on some hypothesis. The term is often used in itting a theoretical distribution to a set of observations.
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 .