 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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Analytic study
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

Assignable cause
The portion of the variability in a set of observations that can be traced to speciic causes, such as operators, materials, or equipment. Also called a special cause.

Bernoulli trials
Sequences of independent trials with only two outcomes, generally called “success” and “failure,” in which the probability of success remains constant.

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

Causal variable
When y fx = ( ) and y is considered to be caused by x, x is sometimes called a causal variable

Central tendency
The tendency of data to cluster around some value. Central tendency is usually expressed by a measure of location such as the mean, median, or mode.

Chisquare test
Any test of signiicance based on the chisquare distribution. The most common chisquare tests are (1) testing hypotheses about the variance or standard deviation of a normal distribution and (2) testing goodness of it of a theoretical distribution to sample data

Components of variance
The individual components of the total variance that are attributable to speciic sources. This usually refers to the individual variance components arising from a random or mixed model analysis of variance.

Conditional probability density function
The probability density function of the conditional probability distribution of a continuous random variable.

Conditional probability distribution
The distribution of a random variable given that the random experiment produces an outcome in an event. The given event might specify values for one or more other random variables

Conidence coeficient
The probability 1?a associated with a conidence interval expressing the probability that the stated interval will contain the true parameter value.

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

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 .

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

Dispersion
The amount of variability exhibited by data

Distribution function
Another name for a cumulative distribution function.

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

F distribution.
The distribution of the random variable deined as the ratio of two independent chisquare random variables, each divided by its number of degrees of freedom.

Fisher’s least signiicant difference (LSD) method
A series of pairwise hypothesis tests of treatment means in an experiment to determine which means differ.

Fraction defective control chart
See P chart