- 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
All possible (subsets) regressions
A method of variable selection in regression that examines all possible subsets of the candidate regressor variables. Eficient computer algorithms have been developed for implementing all possible regressions
A study in which a sample from a population is used to make inference to a future population. Stability needs to be assumed. See Enumerative study
See Arithmetic mean.
An equation for a conditional probability such as PA B ( | ) in terms of the reverse conditional probability PB A ( | ).
Box plot (or box and whisker plot)
A graphical display of data in which the box contains the middle 50% of the data (the interquartile range) with the median dividing it, and the whiskers extend to the smallest and largest values (or some deined lower and upper limits).
Chi-square (or chi-squared) random variable
A continuous random variable that results from the sum of squares of independent standard normal random variables. It is a special case of a gamma random variable.
The probability 1?a associated with a conidence interval expressing the probability that the stated interval will contain the true parameter value.
Another term for the conidence coeficient.
A probability distribution for a continuous random variable.
Continuous random variable.
A random variable with an interval (either inite or ininite) of real numbers for its range.
Cumulative normal distribution function
The cumulative distribution of the standard normal distribution, often denoted as ?( ) x and tabulated in Appendix Table II.
Discrete random variable
A random variable with a inite (or countably ininite) range.
Another name for a cumulative distribution function.
A study in which a sample from a population is used to make inference to the population. See Analytic study
A property of a collection of events that indicates that their union equals the sample space.
A type of experimental design in which every level of one factor is tested in combination with every level of another factor. In general, in a factorial experiment, all possible combinations of factor levels are tested.
Fixed factor (or fixed effect).
In analysis of variance, a factor or effect is considered ixed if all the levels of interest for that factor are included in the experiment. Conclusions are then valid about this set of levels only, although when the factor is quantitative, it is customary to it a model to the data for interpolating between these levels.
A function used in the probability density function of a gamma random variable that can be considered to extend factorials
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 .
In multiple regression, the matrix H XXX X = ( ) ? ? -1 . This a projection matrix that maps the vector of observed response values into a vector of itted values by yˆ = = X X X X y Hy ( ) ? ? ?1 .