- 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: Chi-Square Tests for Goodness of Fit
- Chapter 11.2: Inference for Two-Way 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: Least-Squares 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
`-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).
In a fractional factorial experiment when certain factor effects cannot be estimated uniquely, they are said to be aliased.
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
A distribution with two modes
Bivariate normal distribution
The joint distribution of two normal random variables
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 second-order response surface design in k variables consisting of a two-level factorial, 2k axial runs, and one or more center points. The two-level 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 second-order model.
The variance of the conditional probability distribution of a random variable.
A tabular arrangement expressing the assignment of members of a data set according to two or more categories or classiication criteria
A probability distribution for a continuous random variable.
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 .
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).
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
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.
Another name for a cumulative distribution function.
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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