 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
Get Full SolutionsThe full stepbystep solution to problem in The Practice of Statistics were answered by , our top Statistics solution expert on 03/19/18, 03:52PM. This expansive textbook survival guide covers the following chapters: 44. Since problems from 44 chapters in The Practice of Statistics have been answered, more than 16937 students have viewed full stepbystep answer. This textbook survival guide was created for the textbook: The Practice of Statistics, edition: 5. The Practice of Statistics was written by and is associated to the ISBN: 9781464108730.

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.

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.

Average
See Arithmetic mean.

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

Completely randomized design (or experiment)
A type of experimental design in which the treatments or design factors are assigned to the experimental units in a random manner. In designed experiments, a completely randomized design results from running all of the treatment combinations in random order.

Continuity correction.
A correction factor used to improve the approximation to binomial probabilities from a normal distribution.

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 coeficient
A dimensionless measure of the linear association between two variables, usually lying in the interval from ?1 to +1, with zero indicating the absence of correlation (but not necessarily the independence of the two variables).

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 .

Critical region
In hypothesis testing, this is the portion of the sample space of a test statistic that will lead to rejection of the null hypothesis.

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

Defectsperunit control chart
See U chart

Deming’s 14 points.
A management philosophy promoted by W. Edwards Deming that emphasizes the importance of change and quality

Dependent variable
The response variable in regression or a designed experiment.

Discrete distribution
A probability distribution for a discrete random variable

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

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.

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

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 .