- 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
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
The portion of the variability in a set of observations that can be traced to speciic causes, such as operators, materials, or equipment. Also called a special cause.
Sequences of independent trials with only two outcomes, generally called “success” and “failure,” in which the probability of success remains constant.
Data consisting of counts or observations that can be classiied into categories. The categories may be descriptive.
When y fx = ( ) and y is considered to be caused by x, x is sometimes called a causal variable
The tendency of data to cluster around some value. Central tendency is usually expressed by a measure of location such as the mean, median, or mode.
Any test of signiicance based on the chi-square distribution. The most common chi-square 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
Components of variance
The individual components of the total variance that are attributable to speciic sources. This usually refers to the individual variance components arising from a random or mixed model analysis of variance.
Conditional probability density function
The probability density function of the conditional probability distribution of a continuous random variable.
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
The probability 1?a associated with a conidence interval expressing the probability that the stated interval will contain the true parameter value.
Formulas used to determine the number of elements in sample spaces and events.
A measure of association between two random variables obtained as the expected value of the product of the two random variables around their means; that is, Cov(X Y, ) [( )( )] =? ? E X Y ? ? X Y .
Discrete random variable
A random variable with a inite (or countably ininite) range.
The amount of variability exhibited by data
Another name for a cumulative distribution function.
The variance of an error term or component in a model.
The distribution of the random variable deined as the ratio of two independent chi-square random variables, each divided by its number of degrees of freedom.
Fisher’s least signiicant difference (LSD) method
A series of pair-wise hypothesis tests of treatment means in an experiment to determine which means differ.
Fraction defective control chart
See P chart