- 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
In a fractional factorial experiment when certain factor effects cannot be estimated uniquely, they are said to be aliased.
Attribute control chart
Any control chart for a discrete random variable. See Variables control chart.
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
Sequences of independent trials with only two outcomes, generally called “success” and “failure,” in which the probability of success remains constant.
In experimental design, a group of experimental units or material that is relatively homogeneous. The purpose of dividing experimental units into blocks is to produce an experimental design wherein variability within blocks is smaller than variability between blocks. This allows the factors of interest to be compared in an environment that has less variability than in an unblocked experiment.
Central limit theorem
The simplest form of the central limit theorem states that the sum of n independently distributed random variables will tend to be normally distributed as n becomes large. It is a necessary and suficient condition that none of the variances of the individual random variables are large in comparison to their sum. There are more general forms of the central theorem that allow ininite variances and correlated random variables, and there is a multivariate version of the theorem.
Coeficient of determination
See R 2 .
An experiment in which the treatments (experimental conditions) that are to be studied are included in the experiment. The data from the experiment are used to evaluate the treatments.
Conditional probability density function
The probability density function of the conditional probability distribution of a continuous random variable.
The variance of the conditional probability distribution of a random variable.
The probability 1?a associated with a conidence interval expressing the probability that the stated interval will contain the true parameter value.
An estimator that converges in probability to the true value of the estimated parameter as the sample size increases.
A probability distribution for a continuous random variable.
Continuous uniform random variable
A continuous random variable with range of a inite interval and a constant probability density function.
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.
Another name for factors that are arranged in a factorial experiment.
W. Edwards Deming (1900–1993) was a leader in the use of statistical quality control.
Discrete uniform random variable
A discrete random variable with a inite range and constant probability mass function.
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.
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
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