- Chapter 1: Introduction
- Chapter 10: Point Estimation
- Chapter 11: Interval Estimation
- Chapter 12: Hypothesis Testing
- Chapter 13: Tests of Hypothesis Involving Means, Variances, and Proportions
- Chapter 14: Regression and Correlation
- Chapter 15: Sums and Products
- Chapter 2: Probability
- Chapter 3: Probability Distributions and Probability Densities
- Chapter 4: Mathematical Expectation
- Chapter 5: Special Probability Distributions
- Chapter 6: Special Probability Densities
- Chapter 7: Functions of Random Variables
- Chapter 8: Sampling Distributions
- Chapter 9: Decision Theory
Mathematical Statistics with Applications 8th Edition - Solutions by Chapter
Full solutions for Mathematical Statistics with Applications | 8th Edition
2 k factorial experiment.
A full factorial experiment with k factors and all factors tested at only two levels (settings) each.
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).
The arithmetic mean of a set of numbers x1 , x2 ,…, xn is their sum divided by the number of observations, or ( / )1 1 n xi t n ? = . The arithmetic mean is usually denoted by x , and is often called the average
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 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.
Bivariate normal distribution
The joint distribution of two normal random variables
When y fx = ( ) and y is considered to be caused by x, x is sometimes called a causal variable
Conditional probability density function
The probability density function of the conditional probability distribution of a continuous random variable.
An estimator that converges in probability to the true value of the estimated parameter as the sample size increases.
An expression sometimes used for nonlinear regression models or polynomial regression models.
A parameter in a tabular CUSUM algorithm that is determined from a trade-off between false alarms and the detection of assignable causes.
A subset of effects in a fractional factorial design that deine the aliases in the design.
The response variable in regression or a designed experiment.
Discrete uniform random variable
A discrete random variable with a inite range and constant probability mass function.
The amount of variability exhibited by data
Error of estimation
The difference between an estimated value and the true value.
An analysis of how the variance of the random variable that represents that output of a system depends on the variances of the inputs. A formula exists when the output is a linear function of the inputs and the formula is simpliied if the inputs are assumed to be independent.
The variance of an error term or component in a model.
Estimator (or point estimator)
A procedure for producing an estimate of a parameter of interest. An estimator is usually a function of only sample data values, and when these data values are available, it results in an estimate of the parameter of interest.
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