 Chapter 1: Statistics: The Art and Science of Data
 Chapter 10: Inference in Practice
 Chapter 2: Describing Distributions of Data
 Chapter 3: Modeling Distributions of Data
 Chapter 4: Describing Relationships
 Chapter 5: Sampling and Surveys
 Chapter 6: Designing Experiments
 Chapter 7: Probability: What Are the Chances?
 Chapter 8: Probability Models
 Chapter 9: ntroduction to Inference
Statistics Through Applications 2nd Edition  Solutions by Chapter
Full solutions for Statistics Through Applications  2nd Edition
ISBN: 9781429219747
Statistics Through Applications  2nd 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.

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

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.

Bias
An effect that systematically distorts a statistical result or estimate, preventing it from representing the true quantity of interest.

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

Bivariate distribution
The joint probability distribution of two random variables.

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).

Categorical data
Data consisting of counts or observations that can be classiied into categories. The categories may be descriptive.

Central composite design (CCD)
A secondorder response surface design in k variables consisting of a twolevel factorial, 2k axial runs, and one or more center points. The twolevel factorial portion of a CCD can be a fractional factorial design when k is large. The CCD is the most widely used design for itting a secondorder model.

Conditional probability
The probability of an event given that the random experiment produces an outcome in another event.

Conditional probability mass function
The probability mass function of the conditional probability distribution of a discrete random variable.

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

Conidence interval
If it is possible to write a probability statement of the form PL U ( ) ? ? ? ? = ?1 where L and U are functions of only the sample data and ? is a parameter, then the interval between L and U is called a conidence interval (or a 100 1( )% ? ? conidence interval). The interpretation is that a statement that the parameter ? lies in this interval will be true 100 1( )% ? ? of the times that such a statement is made

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.

Counting techniques
Formulas used to determine the number of elements in sample spaces and events.

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

Discrete random variable
A random variable with a inite (or countably ininite) range.

Error sum of squares
In analysis of variance, this is the portion of total variability that is due to the random component in the data. It is usually based on replication of observations at certain treatment combinations in the experiment. It is sometimes called the residual sum of squares, although this is really a better term to use only when the sum of squares is based on the remnants of a modelitting process and not on replication.

Event
A subset of a sample space.

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.