 Chapter 1: The Nature of Probability and Statistics
 Chapter 11: The Nature of Probability and Statistics
 Chapter 12: The Nature of Probability and Statistics
 Chapter 13: The Nature of Probability and Statistics
 Chapter 14: The Nature of Probability and Statistics
 Chapter 10: Correlation and Regression
 Chapter 101: Correlation and Regression
 Chapter 102: Correlation and Regression
 Chapter 103: Correlation and Regression
 Chapter 104: Correlation and Regression
 Chapter 11: Other ChiSquare Tests
 Chapter 111: Other ChiSquare Tests
 Chapter 112: Other ChiSquare Tests
 Chapter 12: Analysis of Variance
 Chapter 121: Analysis of Variance
 Chapter 122: Analysis of Variance
 Chapter 123: Analysis of Variance
 Chapter 13: Nonparametric Statistics
 Chapter 131: Nonparametric Statistics
 Chapter 132: Nonparametric Statistics
 Chapter 133: Nonparametric Statistics
 Chapter 134: Nonparametric Statistics
 Chapter 135: Nonparametric Statistics
 Chapter 136: Nonparametric Statistics
 Chapter 14: Sampling and Simulation
 Chapter 141: Sampling and Simulation
 Chapter 142: Sampling and Simulation
 Chapter 143: Sampling and Simulation
 Chapter 2: Frequency Distributions and Graphs
 Chapter 21: Frequency Distributions and Graphs
 Chapter 22: Frequency Distributions and Graphs
 Chapter 23: Frequency Distributions and Graphs
 Chapter 3: Data Description
 Chapter 31: Data Description
 Chapter 32: Data Description
 Chapter 33: Data Description
 Chapter 34: Data Description
 Chapter 41: Probability and Counting Rules
 Chapter 42: Probability and Counting Rules
 Chapter 43: Probability and Counting Rules
 Chapter 44: Probability and Counting Rules
 Chapter 45: Probability and Counting Rules
 Chapter 5: Discrete Probability Distributions
 Chapter 51: Discrete Probability Distributions
 Chapter 52: Discrete Probability Distributions
 Chapter 53: Discrete Probability Distributions
 Chapter 54: Discrete Probability Distributions
 Chapter 6: The Normal Distribution
 Chapter 61: The Normal Distribution
 Chapter 62: The Normal Distribution
 Chapter 63: The Normal Distribution
 Chapter 64: The Normal Distribution
 Chapter 7: Confidence Intervals and Sample Size
 Chapter 71: Confidence Intervals and Sample Size
 Chapter 72: Confidence Intervals and Sample Size
 Chapter 73: Confidence Intervals and Sample Size
 Chapter 74: Confidence Intervals and Sample Size
 Chapter 8: Hypothesis Testing
 Chapter 81: Hypothesis Testing
 Chapter 82: Hypothesis Testing
 Chapter 83: Hypothesis Testing
 Chapter 84: Hypothesis Testing
 Chapter 85: Hypothesis Testing
 Chapter 86: Hypothesis Testing
 Chapter 9: Testing the Difference Between Two Means, Two Proportions, and Two Variances
 Chapter 91: Testing the Difference Between Two Means, Two Proportions, and Two Variances
 Chapter 92: Testing the Difference Between Two Means, Two Proportions, and Two Variances
 Chapter 93: Testing the Difference Between Two Means, Two Proportions, and Two Variances
 Chapter 94: Testing the Difference Between Two Means, Two Proportions, and Two Variances
 Chapter 95: Testing the Difference Between Two Means, Two Proportions, and Two Variances
Elementary Statistics: A Step by Step Approach 7th Edition  Solutions by Chapter
Full solutions for Elementary Statistics: A Step by Step Approach  7th Edition
ISBN: 9780073534978
Elementary Statistics: A Step by Step Approach  7th Edition  Solutions by Chapter
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Analysis of variance (ANOVA)
A method of decomposing the total variability in a set of observations, as measured by the sum of the squares of these observations from their average, into component sums of squares that are associated with speciic deined sources of variation

Arithmetic mean
The arithmetic mean of a set of numbers x1 , x2 ,…, xn is their sum divided by the number of observations, or ( / )1 1 n xi t n ? = . The arithmetic mean is usually denoted by x , and is often called the average

Backward elimination
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

Block
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.

Box plot (or box and whisker plot)
A graphical display of data in which the box contains the middle 50% of the data (the interquartile range) with the median dividing it, and the whiskers extend to the smallest and largest values (or some deined lower and upper limits).

Chance cause
The portion of the variability in a set of observations that is due to only random forces and which cannot be traced to speciic sources, such as operators, materials, or equipment. Also called a common cause.

Comparative experiment
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.

Conditional probability mass function
The probability mass function of the conditional probability distribution of a discrete random variable.

Contingency table.
A tabular arrangement expressing the assignment of members of a data set according to two or more categories or classiication criteria

Cook’s distance
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.

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.

Designed experiment
An experiment in which the tests are planned in advance and the plans usually incorporate statistical models. See Experiment

Discrete random variable
A random variable with a inite (or countably ininite) range.

Eficiency
A concept in parameter estimation that uses the variances of different estimators; essentially, an estimator is more eficient than another estimator if it has smaller variance. When estimators are biased, the concept requires modiication.

Enumerative study
A study in which a sample from a population is used to make inference to the population. See Analytic study

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.

Fractional factorial experiment
A type of factorial experiment in which not all possible treatment combinations are run. This is usually done to reduce the size of an experiment with several factors.

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

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.

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 .