- 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
2 k factorial experiment.
A full factorial experiment with k factors and all factors tested at only two levels (settings) each.
In a fractional factorial experiment when certain factor effects cannot be estimated uniquely, they are said to be aliased.
All possible (subsets) regressions
A method of variable selection in regression that examines all possible subsets of the candidate regressor variables. Eficient computer algorithms have been developed for implementing all possible regressions
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.
A method of variable selection in regression that begins with all of the candidate regressor variables in the model and eliminates the insigniicant regressors one at a time until only signiicant regressors remain
Binomial random variable
A discrete random variable that equals the number of successes in a ixed number of Bernoulli trials.
Bivariate normal distribution
The joint distribution of two normal random variables
A horizontal line on a control chart at the value that estimates the mean of the statistic plotted on the chart. See Control chart.
Coeficient of determination
See R 2 .
An experiment in which the treatments (experimental conditions) that are to be studied are included in the experiment. The data from the experiment are used to evaluate the treatments.
The probability of an event given that the random experiment produces an outcome in another event.
In regression, Cook’s distance is a measure of the inluence of each individual observation on the estimates of the regression model parameters. It expresses the distance that the vector of model parameter estimates with the ith observation removed lies from the vector of model parameter estimates based on all observations. Large values of Cook’s distance indicate that the observation is inluential.
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.
A parameter in a tabular CUSUM algorithm that is determined from a trade-off between false alarms and the detection of assignable causes.
A subset of effects in a fractional factorial design that deine the aliases in the design.
Discrete random variable
A random variable with a inite (or countably ininite) range.
The amount of variability exhibited by data
Error sum of squares
In analysis of variance, this is the portion of total variability that is due to the random component in the data. It is usually based on replication of observations at certain treatment combinations in the experiment. It is sometimes called the residual sum of squares, although this is really a better term to use only when the sum of squares is based on the remnants of a model-itting process and not on replication.
Finite population correction factor
A term in the formula for the variance of a hypergeometric random variable.
The geometric mean of a set of n positive data values is the nth root of the product of the data values; that is, g x i n i n = ( ) = / w 1 1 .