 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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2 k p  factorial experiment
A fractional factorial experiment with k factors tested in a 2 ? p fraction with all factors tested at only two levels (settings) each

Adjusted R 2
A variation of the R 2 statistic that compensates for the number of parameters in a regression model. Essentially, the adjustment is a penalty for increasing the number of parameters in the model. Alias. In a fractional factorial experiment when certain factor effects cannot be estimated uniquely, they are said to be aliased.

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

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

Arithmetic mean
The arithmetic mean of a set of numbers x1 , x2 ,…, xn is their sum divided by the number of observations, or ( / )1 1 n xi t n ? = . The arithmetic mean is usually denoted by x , and is often called the average

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

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.

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.

Curvilinear regression
An expression sometimes used for nonlinear regression models or polynomial regression models.

Defect concentration diagram
A quality tool that graphically shows the location of defects on a part or in a process.

Error propagation
An analysis of how the variance of the random variable that represents that output of a system depends on the variances of the inputs. A formula exists when the output is a linear function of the inputs and the formula is simpliied if the inputs are assumed to be independent.

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.

Extra sum of squares method
A method used in regression analysis to conduct a hypothesis test for the additional contribution of one or more variables to a model.

False alarm
A signal from a control chart when no assignable causes are present

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

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

Fractional factorial experiment
A type of factorial experiment in which not all possible treatment combinations are run. This is usually done to reduce the size of an experiment with several factors.

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.