- 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
Probability and Statistics for Engineers and Scientists | 4th Edition - Solutions by ChapterGet Full Solutions
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
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
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
A distribution with two modes
Bivariate normal distribution
The joint distribution of two normal random variables
Coeficient of determination
See R 2 .
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.
The probability 1?a associated with a conidence interval expressing the probability that the stated interval will contain the true parameter value.
A correction factor used to improve the approximation to binomial probabilities from a normal distribution.
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.
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 .
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
Another name for a probability density function
Another name for a cumulative distribution function.
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 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 model that contains only irstorder terms. For example, the irst-order response surface model in two variables is y xx = + ?? ? ? 0 11 2 2 + + . A irst-order model is also called a main effects model