 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 4551 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.

`error (or `risk)
In hypothesis testing, an error incurred by rejecting a null hypothesis when it is actually true (also called a type I error).

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

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

Bimodal distribution.
A distribution with two modes

Bivariate normal distribution
The joint distribution of two normal 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.

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.

Conditional variance.
The variance of the conditional probability distribution of a random variable.

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

Continuous distribution
A probability distribution for a continuous random variable.

Correction factor
A term used for the quantity ( / )( ) 1 1 2 n xi i n ? = that is subtracted from xi i n 2 ? =1 to give the corrected sum of squares deined as (/ ) ( ) 1 1 2 n xx i x i n ? = i ? . The correction factor can also be written as nx 2 .

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).

Critical value(s)
The value of a statistic corresponding to a stated signiicance level as determined from the sampling distribution. For example, if PZ z PZ ( )( .) . ? =? = 0 025 . 1 96 0 025, then z0 025 . = 1 9. 6 is the critical value of z at the 0.025 level of signiicance. Crossed factors. Another name for factors that are arranged in a factorial experiment.

Cumulative sum control chart (CUSUM)
A control chart in which the point plotted at time t is the sum of the measured deviations from target for all statistics up to time t

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

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

Discrete uniform random variable
A discrete random variable with a inite range and constant probability mass function.

Distribution function
Another name for a cumulative distribution function.

Event
A subset of a sample space.

Goodness of fit
In general, the agreement of a set of observed values and a set of theoretical values that depend on some hypothesis. The term is often used in itting a theoretical distribution to a set of observations.
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