 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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2 k factorial experiment.
A full factorial experiment with k factors and all factors tested at only two levels (settings) each.

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

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

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.

Backward elimination
A method of variable selection in regression that begins with all of the candidate regressor variables in the model and eliminates the insigniicant regressors one at a time until only signiicant regressors remain

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

Bivariate normal distribution
The joint distribution of two normal random variables

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.

Coeficient of determination
See R 2 .

Comparative experiment
An experiment in which the treatments (experimental conditions) that are to be studied are included in the experiment. The data from the experiment are used to evaluate the treatments.

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

Cook’s distance
In regression, Cook’s distance is a measure of the inluence of each individual observation on the estimates of the regression model parameters. It expresses the distance that the vector of model parameter estimates with the ith observation removed lies from the vector of model parameter estimates based on all observations. Large values of Cook’s distance indicate that the observation is inluential.

Correlation
In the most general usage, a measure of the interdependence among data. The concept may include more than two variables. The term is most commonly used in a narrow sense to express the relationship between quantitative variables or ranks.

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

Deining relation
A subset of effects in a fractional factorial design that deine the aliases in the design.

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

Dispersion
The amount of variability exhibited by data

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.

Finite population correction factor
A term in the formula for the variance of a hypergeometric random variable.

Geometric mean.
The geometric mean of a set of n positive data values is the nth root of the product of the data values; that is, g x i n i n = ( ) = / w 1 1 .