 Chapter 1: Introduction to Statistics
 Chapter 1.2: Statistical and Critical Thinking
 Chapter 1.3: Types of Data
 Chapter 1.4: Collecting Sample Data
 Chapter 10: Correlation and Regression
 Chapter 10.2: Correlation
 Chapter 10.3: Regression
 Chapter 10.4: Rank Correlation
 Chapter 11: ChiSquare and Analysis of Variance
 Chapter 11.2: GoodnessofFit
 Chapter 11.3: Contingency Tables
 Chapter 11.4: Analysis of Variance
 Chapter 2: Summarizing and Graphing Data
 Chapter 2.2: Frequency Distributions
 Chapter 2.3: Histograms
 Chapter 2.4: Graphs That Enlighten and Graphs That Deceive
 Chapter 3: Statistics for Describing, Exploring, and Comparing Data
 Chapter 3.2: Measures of Center
 Chapter 3.3: Measures of Variation
 Chapter 3.4: Measures of Relative Standing and Boxplots
 Chapter 4: Probability
 Chapter 4.2: Basic Concepts of Probability
 Chapter 4.3: Addition Rule
 Chapter 4.4: Multiplication Rule: Basics
 Chapter 4.5: Multiplication Rule: Complements and Conditional Probability
 Chapter 4.6: Counting
 Chapter 5: Discrete Probability Distributions
 Chapter 5.2: Probability Distributions
 Chapter 5.3: Binomial Probability Distributions
 Chapter 5.4: Parameters for Binomial Distributions
 Chapter 6: Normal Probability Distributions
 Chapter 6.2: The Standard Normal Distribution
 Chapter 6.3: Applications of Normal Distributions
 Chapter 6.4: Sampling Distributions and Estimators
 Chapter 6.5: The Central Limit Theorem
 Chapter 6.6: Assessing Normality
 Chapter 6.7: Normal as Approximation to Binomial
 Chapter 7: Estimates and Sample Sizes
 Chapter 7.2: Estimating a Population Proportion
 Chapter 7.3: Estimating a Population Mean
 Chapter 7.4: Estimating a Population Standard Deviation or Variance
 Chapter 8: Hypothesis Testing
 Chapter 8.2: Basics of Hypothesis Testing
 Chapter 8.3: Testing a Claim About a Proportion
 Chapter 8.4: Testing a Claim about a Mean
 Chapter 8.5: Testing a Claim About a Standard Deviation or Variance
 Chapter 9: Inferences from Two Samples
 Chapter 9.2: Two Proportions
 Chapter 9.3: Two Means: Independent Samples
 Chapter 9.4: Two Dependent Samples (Matched Pairs)
Essentials of Statistics 5th Edition  Solutions by Chapter
Full solutions for Essentials of Statistics  5th Edition
ISBN: 9780321924599
Essentials of Statistics  5th Edition  Solutions by Chapter
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aerror (or arisk)
In hypothesis testing, an error incurred by failing to reject a null hypothesis when it is actually false (also called a type II error).

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.

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

Bivariate distribution
The joint probability distribution of two random variables.

Block
In experimental design, a group of experimental units or material that is relatively homogeneous. The purpose of dividing experimental units into blocks is to produce an experimental design wherein variability within blocks is smaller than variability between blocks. This allows the factors of interest to be compared in an environment that has less variability than in an unblocked experiment.

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.

Chance cause
The portion of the variability in a set of observations that is due to only random forces and which cannot be traced to speciic sources, such as operators, materials, or equipment. Also called a common cause.

Combination.
A subset selected without replacement from a set used to determine the number of outcomes in events and sample spaces.

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.

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.

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 .

Covariance matrix
A square matrix that contains the variances and covariances among a set of random variables, say, X1 , X X 2 k , , … . The main diagonal elements of the matrix are the variances of the random variables and the offdiagonal elements are the covariances between Xi and Xj . Also called the variancecovariance matrix. When the random variables are standardized to have unit variances, the covariance matrix becomes the correlation matrix.

Cumulative normal distribution function
The cumulative distribution of the standard normal distribution, often denoted as ?( ) x and tabulated in Appendix Table II.

Error of estimation
The difference between an estimated value and the true value.

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.

Factorial experiment
A type of experimental design in which every level of one factor is tested in combination with every level of another factor. In general, in a factorial experiment, all possible combinations of factor levels are tested.

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

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

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