 Chapter 1: Introduction to Statistics
 Chapter 11: Statistical and Critical Thinking
 Chapter 12: Types of Data
 Chapter 13: Collecting Sample Data
 Chapter 10: Correlation and Regression
 Chapter 101: Correlation
 Chapter 102: Regression
 Chapter 103: Prediction Intervals and Variation
 Chapter 104: Multiple Regression
 Chapter 105: Nonlinear Regression
 Chapter 11: GoodnessofFit and Contingency Tables
 Chapter 111: GoodnessofFit
 Chapter 112: Contingency Tables
 Chapter 12: Analysis of Variance
 Chapter 121: OneWay ANOVA
 Chapter 122: TwoWay ANOVA
 Chapter 13: Nonparametric Tests
 Chapter 132: Sign Test
 Chapter 133: Wilcoxon SignedRanks Test for Matched Pairs
 Chapter 134: Wilcoxon RankSum Test for Two Independent Samples
 Chapter 135: KruskalWallis Test for Three or More Samples
 Chapter 136: Rank Correlation
 Chapter 137: Runs Test for Randomness
 Chapter 14: Statistical Process Control
 Chapter 141: Control Charts for Variation and Mean
 Chapter 142: Control Charts for Attributes
 Chapter 2: Exploring Data with Tables and Graphs
 Chapter 21: Frequency Distributions for Organizing and Summarizing Data
 Chapter 22: Histograms
 Chapter 23: Graphs That Enlighten and Graphs That Deceive
 Chapter 24: Scatterplots, Correlation, and Regression
 Chapter 3: Describing, Exploring, and Comparing Data
 Chapter 31: Measures of Center
 Chapter 32: Measures of Variation
 Chapter 33: Measures of Relative Standing and Boxplots
 Chapter 4: Probability
 Chapter 41: Basic Concepts of Probability
 Chapter 42: Addition Rule and Multiplication Rule
 Chapter 43: Complements, Conditional Probability, and Bayes’ Theorem
 Chapter 44: Counting
 Chapter 45: Probabilities Through Simulations (available at www.TriolaStats.com)
 Chapter 5: Discrete Probability Distributions
 Chapter 51: Probability Distributions
 Chapter 52: Binomial Probability Distributions
 Chapter 53: Poisson Probability Distributions
 Chapter 6: Normal Probability Distributions
 Chapter 61: The Standard Normal Distribution
 Chapter 62: Real Applications of Normal Distributions
 Chapter 63: Sampling Distributions and Estimators
 Chapter 64: The Central Limit Theorem
 Chapter 65: Assessing Normality
 Chapter 66: Normal as Approximation to Binomial
 Chapter 7: Estimating Parameters and Determining Sample Sizes
 Chapter 71: Estimating a Population Proportion
 Chapter 72: Estimating a Population Mean
 Chapter 73: Estimating a Population Standard Deviation or Variance
 Chapter 74: Bootstrapping: Using Technology for Estimates
 Chapter 8: Hypothesis Testing
 Chapter 81: Basics of Hypothesis Testing
 Chapter 82: Testing a Claim About a Proportion
 Chapter 83: Testing a Claim About a Mean
 Chapter 84: Testing a Claim About a Standard Deviation or Variance
 Chapter 9: Inferences from Two Samples
 Chapter 91: Two Proportions
 Chapter 92: Two Means: Independent Samples
 Chapter 93: Two Dependent Samples (Matched Pairs)
 Chapter 94: Two Variances or Standard Deviations
Elementary Statistics 13th Edition  Solutions by Chapter
Full solutions for Elementary Statistics  13th Edition
ISBN: 9780134462455
Elementary Statistics  13th Edition  Solutions by Chapter
Get Full SolutionsThe full stepbystep solution to problem in Elementary Statistics were answered by Aimee Notetaker, our top Statistics solution expert on 03/25/22, 03:20PM. Since problems from 67 chapters in Elementary Statistics have been answered, more than 4565 students have viewed full stepbystep answer. This expansive textbook survival guide covers the following chapters: 67. Elementary Statistics was written by Aimee Notetaker and is associated to the ISBN: 9780134462455. This textbook survival guide was created for the textbook: Elementary Statistics, edition: 13.

Addition rule
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).

Assignable cause
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.

Attribute
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.

Conditional mean
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

Confounding
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.

Contour plot
A twodimensional graphic used for a bivariate probability density function that displays curves for which the probability density function is constant.

Correlation
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.

Correlation matrix
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 offdiagonal elements rij are the correlations between Xi and Xj .

Decision interval
A parameter in a tabular CUSUM algorithm that is determined from a tradeoff 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.

Density function
Another name for a probability density function

Error mean square
The error sum of squares divided by its number of degrees of freedom.

Error variance
The variance of an error term or component in a model.

Exhaustive
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.

Forward selection
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.

Generating function
A function that is used to determine properties of the probability distribution of a random variable. See Momentgenerating function

Generator
Effects in a fractional factorial experiment that are used to construct the experimental tests used in the experiment. The generators also deine the aliases.

Hat matrix.
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 .