- Chapter 10: Statistical Inference for Two Samples
- Chapter 11: Simple Linear Regression and Correlation
- Chapter 12: Multiple Linear Regression
- Chapter 13: Design and Analysis of Single-Factor Experiments: The Analysis of Variance
- Chapter 14: Design of Experiments with Several Factors
- Chapter 15: Statistical Quality Control
- Chapter 2: Probability
- Chapter 3: Discrete Random Variables and Probability Distributions
- Chapter 4: Continuous Random Variables and Probability Distributions
- Chapter 5: Joint Probability Distributions
- Chapter 6: Descriptive Statistics
- Chapter 7: Sampling Distributions and Point Estimation of Parameters
- Chapter 8: Statistical Intervals for a Single Sample
- Chapter 9: Tests of Hypotheses for a Single Sample
Applied Statistics and Probability for Engineers 5th Edition - Solutions by Chapter
Full solutions for Applied Statistics and Probability for Engineers | 5th Edition
Applied Statistics and Probability for Engineers | 5th Edition - Solutions by ChapterGet Full Solutions
a-error (or a-risk)
In hypothesis testing, an error incurred by failing to reject a null hypothesis when it is actually false (also called a type II error).
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
An estimator for a parameter obtained from a Bayesian method that uses a prior distribution for the parameter along with the conditional distribution of the data given the parameter to obtain the posterior distribution of the parameter. The estimator is obtained from the posterior distribution.
A distribution with two modes
The joint probability distribution of two random variables.
Bivariate normal distribution
The joint distribution of two normal random variables
Coeficient of determination
See R 2 .
The variance of the conditional probability distribution of a random variable.
A tabular arrangement expressing the assignment of members of a data set according to two or more categories or classiication criteria
Continuous uniform random variable
A continuous random variable with range of a inite interval and a constant probability density function.
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 .
Deming’s 14 points.
A management philosophy promoted by W. Edwards Deming that emphasizes the importance of change and quality
A model to relate a response to one or more regressors or factors that is developed from data obtained from the system.
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 signal from a control chart when no assignable causes are present
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
Geometric random variable
A discrete random variable that is the number of Bernoulli trials until a success occurs.
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 .