 Chapter 1: Exploring Data
 Chapter 1.1: Analyzing Categorical Data
 Chapter 1.2: Displaying Quantitative Data with Graphs
 Chapter 1.3: Describing Quantitative Data with Numbers
 Chapter 10: Comparing Two Populations or Groups
 Chapter 10.1: Comparing Two Proportions
 Chapter 10.2: Comparing Two Means
 Chapter 11: Inference for Ditribution of Categorical Data
 Chapter 11.1: ChiSquare Tests for Goodness of Fit
 Chapter 11.2: Inference for TwoWay Tables
 Chapter 12: More About Regression
 Chapter 12.1: Inference for Linear Regression
 Chapter 12.2: Transforming to Achieve Linearity
 Chapter 2: Modeling Distributions of Data
 Chapter 2.1: Describing Location in a Distribution
 Chapter 2.2: Density Curves and Normal Distributions
 Chapter 3: Describing Relationships
 Chapter 3.1: Scatterplots and Correlation
 Chapter 3.2: LeastSquares Regression
 Chapter 4: Designing Studies
 Chapter 4.1: Sampling and Surveys
 Chapter 4.2: Experiments
 Chapter 4.3: Using Studies Wisely
 Chapter 5: Probability: What Are The Chances
 Chapter 5.1: Randomness, Probability, and Simulation
 Chapter 5.2: Probability Rules
 Chapter 5.3: Conditional Probability and Independence
 Chapter 6: Random Variables
 Chapter 6.1: Discrete and Continuous Random Variables
 Chapter 6.2: Transforming and Combining Random Variables
 Chapter 6.3: Binomial and Geometric Random Variables
 Chapter 7: Sampling Distributions
 Chapter 7.1: What Is a Sampling Distribution?
 Chapter 7.2: Sample Proportions
 Chapter 7.3: Sample Means
 Chapter 8: Estimating With Confidence
 Chapter 8.1: Confidence Intervals: The Basics
 Chapter 8.2: Estimating a Population Proportion
 Chapter 8.3: Estimating a Population Mean
 Chapter 9: Testing A Claim
 Chapter 9.1: Significance Tests: The Basics
 Chapter 9.2: Tests about a Population Proportion
 Chapter 9.3: Tests about a Population Mean
 Chapter Introduction: Data Analysis: Making Sense of Data
The Practice of Statistics 5th Edition  Solutions by Chapter
Full solutions for The Practice of Statistics  5th Edition
ISBN: 9781464108730
The Practice of Statistics  5th 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.

Alias
In a fractional factorial experiment when certain factor effects cannot be estimated uniquely, they are said to be aliased.

Analysis of variance (ANOVA)
A method of decomposing the total variability in a set of observations, as measured by the sum of the squares of these observations from their average, into component sums of squares that are associated with speciic deined sources of variation

Attribute
A qualitative characteristic of an item or unit, usually arising in quality control. For example, classifying production units as defective or nondefective results in attributes data.

Biased estimator
Unbiased estimator.

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

Conidence level
Another term for the conidence coeficient.

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

Control chart
A graphical display used to monitor a process. It usually consists of a horizontal center line corresponding to the incontrol value of the parameter that is being monitored and lower and upper control limits. The control limits are determined by statistical criteria and are not arbitrary, nor are they related to speciication limits. If sample points fall within the control limits, the process is said to be incontrol, or free from assignable causes. Points beyond the control limits indicate an outofcontrol process; that is, assignable causes are likely present. This signals the need to ind and remove the assignable causes.

Covariance
A measure of association between two random variables obtained as the expected value of the product of the two random variables around their means; that is, Cov(X Y, ) [( )( )] =? ? E X Y ? ? X Y .

Demingâ€™s 14 points.
A management philosophy promoted by W. Edwards Deming that emphasizes the importance of change and quality

Distribution free method(s)
Any method of inference (hypothesis testing or conidence interval construction) that does not depend on the form of the underlying distribution of the observations. Sometimes called nonparametric method(s).

Error variance
The variance of an error term or component in a model.

Event
A subset of a sample space.

Exhaustive
A property of a collection of events that indicates that their union equals the sample space.

Firstorder model
A model that contains only irstorder terms. For example, the irstorder response surface model in two variables is y xx = + ?? ? ? 0 11 2 2 + + . A irstorder model is also called a main effects model

Fraction defective control chart
See P chart

Gaussian distribution
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

Generating function
A function that is used to determine properties of the probability distribution of a random variable. See Momentgenerating function

Generator
Effects in a fractional factorial experiment that are used to construct the experimental tests used in the experiment. The generators also deine the aliases.