- Chapter 1: Overview and Descriptive Statistics
- Chapter 10: The Analysis of Variance
- Chapter 11: Multifactor of Analysis of Variance
- Chapter 12: Simple Linear Regression and Correlation
- Chapter 13: Nonlinear and Mutiple Regression
- Chapter 14: Goodness-of-Fit Tests and Categorial Data Analysis
- Chapter 15: Distribution-Free Procedures
- Chapter 16: Quality Control Methods
- Chapter 2: Probability
- Chapter 3: Discrete Random Variables and Probability Distributions
- Chapter 4: Continuous Random Variables and Probability Distributions
- Chapter 5: Joint Probability Distributions and Random Samples
- Chapter 6: Point Estimation
- Chapter 7: Statistical Intervals Based on a Single Sample
- Chapter 8: Tests on Hypotheses Based on a Single Sample
- Chapter 9: Inferences Based on Two Samples
- Chapter SE1: Sample Exams
- Chapter SE2: Sample Exams
- Chapter SE3: Sample Exams
- Chapter SE4: Sample Exams
- Chapter SE5: Sample Exams
- Chapter SE6: Sample Exams
- Chapter SE7: Sample Exams
Probability and Statistics for Engineering and the Sciences (with Student Suite Online) 7th Edition - Solutions by Chapter
Full solutions for Probability and Statistics for Engineering and the Sciences (with Student Suite Online) | 7th Edition
Probability and Statistics for Engineering and the Sciences (with Student Suite Online) | 7th Edition - Solutions by ChapterGet Full Solutions
`-error (or `-risk)
In hypothesis testing, an error incurred by rejecting a null hypothesis when it is actually true (also called a type I error).
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
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.
Sequences of independent trials with only two outcomes, generally called “success” and “failure,” in which the probability of success remains constant.
Binomial random variable
A discrete random variable that equals the number of successes in a ixed number of Bernoulli trials.
A tabular arrangement expressing the assignment of members of a data set according to two or more categories or classiication criteria
A probability distribution for a continuous random variable.
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.
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
Defects-per-unit control chart
See U chart
W. Edwards Deming (1900–1993) was a leader in the use of statistical quality control.
The amount of variability exhibited by data
Another name for a cumulative distribution function.
Error mean square
The error sum of squares divided by its number of degrees of freedom.
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
A method of variable selection in regression, where variables are inserted one at a time into the model until no other variables that contribute signiicantly to the model can be found.
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
The harmonic mean of a set of data values is the reciprocal of the arithmetic mean of the reciprocals of the data values; that is, h n x i n i = ? ? ? ? ? = ? ? 1 1 1 1 g .