 Chapter 1: Probability
 Chapter 10: Summarizing Data
 Chapter 11: Comparing Two Samples
 Chapter 12: The Analysis of Variance
 Chapter 13: The Analysis of Categorical Data
 Chapter 14: Linear Least Squares
 Chapter 2: Random Variables
 Chapter 3: Joint Distributions
 Chapter 4: Expected Values
 Chapter 5: Limit Theorems
 Chapter 6: Distributions Derived from the Normal Distribution
 Chapter 7: Survey Sampling
 Chapter 8: Estimation of Parameters and Fitting of Probability Distributions
 Chapter 9: Testing Hypotheses and Assessing Goodness of Fit
Mathematical Statistics and Data Analysis 3rd Edition  Solutions by Chapter
Full solutions for Mathematical Statistics and Data Analysis  3rd Edition
ISBN: 9788131519547
Mathematical Statistics and Data Analysis  3rd Edition  Solutions by Chapter
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Additivity property of x 2
If two independent random variables X1 and X2 are distributed as chisquare with v1 and v2 degrees of freedom, respectively, Y = + X X 1 2 is a chisquare random variable with u = + v v 1 2 degrees of freedom. This generalizes to any number of independent chisquare random variables.

Asymptotic relative eficiency (ARE)
Used to compare hypothesis tests. The ARE of one test relative to another is the limiting ratio of the sample sizes necessary to obtain identical error probabilities for the two procedures.

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.

Binomial random variable
A discrete random variable that equals the number of successes in a ixed number of Bernoulli trials.

C chart
An attribute control chart that plots the total number of defects per unit in a subgroup. Similar to a defectsperunit or U chart.

Center line
A horizontal line on a control chart at the value that estimates the mean of the statistic plotted on the chart. See Control chart.

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.

Conditional variance.
The variance of the conditional probability distribution of a random variable.

Consistent estimator
An estimator that converges in probability to the true value of the estimated parameter as the sample size increases.

Control limits
See Control chart.

Curvilinear regression
An expression sometimes used for nonlinear regression models or polynomial regression models.

Decision interval
A parameter in a tabular CUSUM algorithm that is determined from a tradeoff between false alarms and the detection of assignable causes.

Empirical model
A model to relate a response to one or more regressors or factors that is developed from data obtained from the system.

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.

Experiment
A series of tests in which changes are made to the system under study

Exponential random variable
A series of tests in which changes are made to the system under study

Ftest
Any test of signiicance involving the F distribution. The most common Ftests are (1) testing hypotheses about the variances or standard deviations of two independent normal distributions, (2) testing hypotheses about treatment means or variance components in the analysis of variance, and (3) testing signiicance of regression or tests on subsets of parameters in a regression model.

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.

Fraction defective control chart
See P chart

Hat matrix.
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 .