- Chapter 1: The Nature of Probability and Statistics
- Chapter 10: Review Execises
- Chapter 10-1: Scatter Plots and Correlation
- Chapter 10-2: Regression
- Chapter 10-3: Coefficient of Determination and Standard Error of the Estimate
- Chapter 10-4: Multiple Regression (Optional
- Chapter 11: Review Execises
- Chapter 11-1: Test for Goodness of Fit
- Chapter 11-2: Tests Using Contingency Tables
- Chapter 12: Review Execises
- Chapter 12-1: One-Way Analysis of Variance
- Chapter 12-2: The Scheff Test and the Tukey Test
- Chapter 12-3: Two-Way Analysis of Variance
- Chapter 13: Review Execises
- Chapter 13-1: Advantages and Disadvantages of Nonparametric Methods
- Chapter 13-2: The Sign Test
- Chapter 13-3: The Wilcoxon Rank Sum Test
- Chapter 13-4: The Wilcoxon Signed-Rank Test
- Chapter 13-5: The Kruskal-Wallis Test
- Chapter 13-6: The Spearman Rank Correlation Coefficient and the Runs Test
- Chapter 14: Review Execises
- Chapter 14-1: Common Sampling Techniques
- Chapter 14-2: Surveys and Questionnaire Design
- Chapter 14-3: Simulation Techniques and the Monte Carlo Method
- Chapter 2: Frequency Distributions and Graphs
- Chapter 2-1: Organizing Data
- Chapter 2-2: Histograms, Frequency Polygons, and Ogives
- Chapter 2-3: Other Types of Graphs
- Chapter 3: Data Description
- Chapter 3-1: Measures of Central Tendency
- Chapter 3-2: Measures of Variation
- Chapter 3-3: Measures of Position
- Chapter 3-4: Exploratory Data Analysis
- Chapter 4: Probability and Counting Rules
- Chapter 4-1: Sample Spaces and Probability
- Chapter 4-2: The Addition Rules for Probability
- Chapter 4-3: The Multiplication Rules and Conditional Probability
- Chapter 4-4: Counting Rules
- Chapter 4-5: Probability and Counting Rules
- Chapter 5: Review Execises
- Chapter 5-1: Probability Distributions
- Chapter 5-2: Mean, Variance, Standard Deviation, and Expectation
- Chapter 5-3: The Binomial Distribution
- Chapter 5-4: Other Types of Distributions (Optional)
- Chapter 6: Review Execises
- Chapter 6-1: Normal Distributions
- Chapter 6-2: Applications of the Normal Distribution
- Chapter 6-3: The Central Limit Theorem
- Chapter 6-4: The Normal Approximation to the Binomial Distribution
- Chapter 7: Review Execises
- Chapter 7-1: Confidence Intervals for the Mean When s Is Known
- Chapter 7-2: Confidence Intervals for the Mean When s Is Unknown
- Chapter 7-3: Confidence Intervals and Sample Size for Proportions
- Chapter 7-4: Confidence Intervals for Variances and Standard Deviations
- Chapter 8: Review Execises
- Chapter 8-1: Steps in Hypothesis TestingTraditional Method
- Chapter 8-2: z Test for a Mean
- Chapter 8-3: t Test for a Mean
- Chapter 8-4: z Test for a Proportion
- Chapter 8-5: x2 Test for a Variance or Standard Deviation
- Chapter 8-6: Additional Topics Regarding Hypothesis Testing
- Chapter 9: Review Execises
- Chapter 9-1: Testing the Difference Between Two Means: Using the z Test
- Chapter 9-2: Testing the Difference Between Two Means of Independent Samples: Using the t Test
- Chapter 9-3: Testing the Difference Between Two Means: Dependent Samples
- Chapter 9-4: Testing the Difference Between Proportions
- Chapter 9-5: Testing the Difference Between Two Variances
Elementary Statistics: A Step by Step Approach 8th ed. 8th Edition - Solutions by Chapter
Full solutions for Elementary Statistics: A Step by Step Approach 8th ed. | 8th Edition
Elementary Statistics: A Step by Step Approach 8th ed. | 8th Edition - Solutions by ChapterGet Full Solutions
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
Asymptotic relative eficiency (ARE)
Used to compare hypothesis tests. The ARE of one test relative to another is the limiting ratio of the sample sizes necessary to obtain identical error probabilities for the two procedures.
Completely randomized design (or experiment)
A type of experimental design in which the treatments or design factors are assigned to the experimental units in a random manner. In designed experiments, a completely randomized design results from running all of the treatment combinations in random order.
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.
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 probability distribution for a continuous random variable.
See Control chart.
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.
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 .
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
A probability distribution for a discrete random variable
Another name for a cumulative distribution function.
A series of tests in which changes are made to the system under study
Finite population correction factor
A term in the formula for the variance of a hypergeometric random variable.
A model that contains only irstorder terms. For example, the irst-order response surface model in two variables is y xx = + ?? ? ? 0 11 2 2 + + . A irst-order model is also called a main effects model
Effects in a fractional factorial experiment that are used to construct the experimental tests used in the experiment. The generators also deine the aliases.
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 .
The harmonic mean of a set of data values is the reciprocal of the arithmetic mean of the reciprocals of the data values; that is, h n x i n i = ? ? ? ? ? = ? ? 1 1 1 1 g .
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