 Chapter 1: Probability Theory
 Chapter 10: Discrete Data Analysis
 Chapter 11: The Analysis of Variance
 Chapter 12: Simple Linear Regression and Correlation
 Chapter 13: Multiple Linear Regression and Nonlinear Regression
 Chapter 14: Multifactor Experimental Design and Analysis
 Chapter 15: Nonparametric Statistical Analysis
 Chapter 16: Quality Control Methods
 Chapter 17: Reliability Analysis and Life Testing
 Chapter 2: Random Variables
 Chapter 3: Discrete Probability Distributions
 Chapter 4: Continuous Probability Distributions
 Chapter 5: The Normal Distribution
 Chapter 6: Descriptive Statistics
 Chapter 7: Statistical Estimation and Sampling Distributions
 Chapter 8: Inferences on a Population Mean
 Chapter 9: Comparing Two Population Means
Probability and Statistics for Engineers and Scientists 4th Edition  Solutions by Chapter
Full solutions for Probability and Statistics for Engineers and Scientists  4th Edition
ISBN: 9781111827045
Probability and Statistics for Engineers and Scientists  4th Edition  Solutions by Chapter
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Acceptance region
In hypothesis testing, a region in the sample space of the test statistic such that if the test statistic falls within it, the null hypothesis cannot be rejected. This terminology is used because rejection of H0 is always a strong conclusion and acceptance of H0 is generally a weak conclusion

Alternative hypothesis
In statistical hypothesis testing, this is a hypothesis other than the one that is being tested. The alternative hypothesis contains feasible conditions, whereas the null hypothesis speciies conditions that are under test

Arithmetic mean
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

Biased estimator
Unbiased estimator.

Bimodal distribution.
A distribution with two modes

Bivariate normal distribution
The joint distribution of two normal random variables

Coeficient of determination
See R 2 .

Confounding
When a factorial experiment is run in blocks and the blocks are too small to contain a complete replicate of the experiment, one can run a fraction of the replicate in each block, but this results in losing information on some effects. These effects are linked with or confounded with the blocks. In general, when two factors are varied such that their individual effects cannot be determined separately, their effects are said to be confounded.

Conidence coeficient
The probability 1?a associated with a conidence interval expressing the probability that the stated interval will contain the true parameter value.

Continuity correction.
A correction factor used to improve the approximation to binomial probabilities from a normal distribution.

Contrast
A linear function of treatment means with coeficients that total zero. A contrast is a summary of treatment means that is of interest in an experiment.

Cook’s distance
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.

Correlation matrix
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 offdiagonal elements rij are the correlations between Xi and Xj .

Critical value(s)
The value of a statistic corresponding to a stated signiicance level as determined from the sampling distribution. For example, if PZ z PZ ( )( .) . ? =? = 0 025 . 1 96 0 025, then z0 025 . = 1 9. 6 is the critical value of z at the 0.025 level of signiicance. Crossed factors. Another name for factors that are arranged in a factorial experiment.

Cumulative sum control chart (CUSUM)
A control chart in which the point plotted at time t is the sum of the measured deviations from target for all statistics up to time t

Density function
Another name for a probability density function

Distribution function
Another name for a cumulative distribution function.

Error propagation
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.

F distribution.
The distribution of the random variable deined as the ratio of two independent chisquare random variables, each divided by its number of degrees of freedom.

Firstorder model
A model that contains only irstorder terms. For example, the irstorder response surface model in two variables is y xx = + ?? ? ? 0 11 2 2 + + . A irstorder model is also called a main effects model