- Chapter 1: Introduction to Statistics
- Chapter 1-1: Statistical and Critical Thinking
- Chapter 1-2: Types of Data
- Chapter 1-3: Collecting Sample Data
- Chapter 10: Correlation and Regression
- Chapter 10-1: Correlation
- Chapter 10-2: Regression
- Chapter 10-3: Prediction Intervals and Variation
- Chapter 10-4: Multiple Regression
- Chapter 10-5: Nonlinear Regression
- Chapter 11: Goodness-of-Fit and Contingency Tables
- Chapter 11-1: Goodness-of-Fit
- Chapter 11-2: Contingency Tables
- Chapter 12: Analysis of Variance
- Chapter 12-1: One-Way ANOVA
- Chapter 12-2: Two-Way ANOVA
- Chapter 13: Nonparametric Tests
- Chapter 13-2: Sign Test
- Chapter 13-3: Wilcoxon Signed-Ranks Test for Matched Pairs
- Chapter 13-4: Wilcoxon Rank-Sum Test for Two Independent Samples
- Chapter 13-5: Kruskal-Wallis Test for Three or More Samples
- Chapter 13-6: Rank Correlation
- Chapter 13-7: Runs Test for Randomness
- Chapter 14: Statistical Process Control
- Chapter 14-1: Control Charts for Variation and Mean
- Chapter 14-2: Control Charts for Attributes
- Chapter 2: Exploring Data with Tables and Graphs
- Chapter 2-1: Frequency Distributions for Organizing and Summarizing Data
- Chapter 2-2: Histograms
- Chapter 2-3: Graphs That Enlighten and Graphs That Deceive
- Chapter 2-4: Scatterplots, Correlation, and Regression
- Chapter 3: Describing, Exploring, and Comparing Data
- Chapter 3-1: Measures of Center
- Chapter 3-2: Measures of Variation
- Chapter 3-3: Measures of Relative Standing and Boxplots
- Chapter 4: Probability
- Chapter 4-1: Basic Concepts of Probability
- Chapter 4-2: Addition Rule and Multiplication Rule
- Chapter 4-3: Complements, Conditional Probability, and Bayes’ Theorem
- Chapter 4-4: Counting
- Chapter 4-5: Probabilities Through Simulations (available at www.TriolaStats.com)
- Chapter 5: Discrete Probability Distributions
- Chapter 5-1: Probability Distributions
- Chapter 5-2: Binomial Probability Distributions
- Chapter 5-3: Poisson Probability Distributions
- Chapter 6: Normal Probability Distributions
- Chapter 6-1: The Standard Normal Distribution
- Chapter 6-2: Real Applications of Normal Distributions
- Chapter 6-3: Sampling Distributions and Estimators
- Chapter 6-4: The Central Limit Theorem
- Chapter 6-5: Assessing Normality
- Chapter 6-6: Normal as Approximation to Binomial
- Chapter 7: Estimating Parameters and Determining Sample Sizes
- Chapter 7-1: Estimating a Population Proportion
- Chapter 7-2: Estimating a Population Mean
- Chapter 7-3: Estimating a Population Standard Deviation or Variance
- Chapter 7-4: Bootstrapping: Using Technology for Estimates
- Chapter 8: Hypothesis Testing
- Chapter 8-1: Basics of Hypothesis Testing
- Chapter 8-2: Testing a Claim About a Proportion
- Chapter 8-3: Testing a Claim About a Mean
- Chapter 8-4: Testing a Claim About a Standard Deviation or Variance
- Chapter 9: Inferences from Two Samples
- Chapter 9-1: Two Proportions
- Chapter 9-2: Two Means: Independent Samples
- Chapter 9-3: Two Dependent Samples (Matched Pairs)
- Chapter 9-4: Two Variances or Standard Deviations
Elementary Statistics 13th Edition - Solutions by Chapter
Full solutions for Elementary Statistics | 13th Edition
A formula used to determine the probability of the union of two (or more) events from the probabilities of the events and their intersection(s).
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 qualitative characteristic of an item or unit, usually arising in quality control. For example, classifying production units as defective or nondefective results in attributes data.
The mean of the conditional probability distribution of a 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
When a factorial experiment is run in blocks and the blocks are too small to contain a complete replicate of the experiment, one can run a fraction of the replicate in each block, but this results in losing information on some effects. These effects are linked with or confounded with the blocks. In general, when two factors are varied such that their individual effects cannot be determined separately, their effects are said to be confounded.
A two-dimensional graphic used for a bivariate probability density function that displays curves for which the probability density function is constant.
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 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 off-diagonal elements rij are the correlations between Xi and Xj .
A parameter in a tabular CUSUM algorithm that is determined from a trade-off between false alarms and the detection of assignable causes.
Defect concentration diagram
A quality tool that graphically shows the location of defects on a part or in a process.
Another name for a probability density function
Error mean square
The error sum of squares divided by its number of degrees of freedom.
The variance of an error term or component in a model.
A property of a collection of events that indicates that their union equals the sample space.
Extra sum of squares method
A method used in regression analysis to conduct a hypothesis test for the additional contribution of one or more variables to a model.
A method of variable selection in regression, where variables are inserted one at a time into the model until no other variables that contribute signiicantly to the model can be found.
A function that is used to determine properties of the probability distribution of a random variable. See Moment-generating function
Effects in a fractional factorial experiment that are used to construct the experimental tests used in the experiment. The generators also deine the aliases.
In multiple regression, the matrix H XXX X = ( ) ? ? -1 . This a projection matrix that maps the vector of observed response values into a vector of itted values by yˆ = = X X X X y Hy ( ) ? ? ?1 .