 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

Alternative hypothesis
In statistical hypothesis testing, this is a hypothesis other than the one that is being tested. The alternative hypothesis contains feasible conditions, whereas the null hypothesis speciies conditions that are under test

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

Biased estimator
Unbiased estimator.

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

Central composite design (CCD)
A secondorder response surface design in k variables consisting of a twolevel factorial, 2k axial runs, and one or more center points. The twolevel factorial portion of a CCD can be a fractional factorial design when k is large. The CCD is the most widely used design for itting a secondorder model.

Coeficient of determination
See R 2 .

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

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

Contingency table.
A tabular arrangement expressing the assignment of members of a data set according to two or more categories or classiication criteria

Continuous uniform random variable
A continuous random variable with range of a inite interval and a constant probability density function.

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.

Correlation matrix
A square matrix that contains the correlations among a set of random variables, say, XX X 1 2 k , ,…, . The main diagonal elements of the matrix are unity and the offdiagonal elements rij are the correlations between Xi and Xj .

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

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

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

Dispersion
The amount of variability exhibited by data

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.

Harmonic mean
The harmonic mean of a set of data values is the reciprocal of the arithmetic mean of the reciprocals of the data values; that is, h n x i n i = ? ? ? ? ? = ? ? 1 1 1 1 g .