- 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
Average run length, or ARL
The average number of samples taken in a process monitoring or inspection scheme until the scheme signals that the process is operating at a level different from the level in which it began.
Sequences of independent trials with only two outcomes, generally called “success” and “failure,” in which the probability of success remains constant.
Binomial random variable
A discrete random variable that equals the number of successes in a ixed number of Bernoulli trials.
Central composite design (CCD)
A second-order response surface design in k variables consisting of a two-level factorial, 2k axial runs, and one or more center points. The two-level factorial portion of a CCD can be a fractional factorial design when k is large. The CCD is the most widely used design for itting a second-order model.
The tendency of data to cluster around some value. Central tendency is usually expressed by a measure of location such as the mean, median, or mode.
The probability of an event given that the random experiment produces an outcome in another event.
The probability 1?a associated with a conidence interval expressing the probability that the stated interval will contain the true parameter value.
If it is possible to write a probability statement of the form PL U ( ) ? ? ? ? = ?1 where L and U are functions of only the sample data and ? is a parameter, then the interval between L and U is called a conidence interval (or a 100 1( )% ? ? conidence interval). The interpretation is that a statement that the parameter ? lies in this interval will be true 100 1( )% ? ? of the times that such a statement is made
See Control chart.
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.
Cumulative distribution function
For a random variable X, the function of X deined as PX x ( ) ? that is used to specify the probability distribution.
Cumulative normal distribution function
The cumulative distribution of the standard normal distribution, often denoted as ?( ) x and tabulated in Appendix Table II.
Cumulative sum control chart (CUSUM)
A control chart in which the point plotted at time t is the sum of the measured deviations from target for all statistics up to time t
Used in statistical quality control, a defect is a particular type of nonconformance to speciications or requirements. Sometimes defects are classiied into types, such as appearance defects and functional defects.
Defects-per-unit control chart
See U chart
A matrix that provides the tests that are to be conducted in an experiment.
Any test of signiicance involving the F distribution. The most common F-tests are (1) testing hypotheses about the variances or standard deviations of two independent normal distributions, (2) testing hypotheses about treatment means or variance components in the analysis of variance, and (3) testing signiicance of regression or tests on subsets of parameters in a regression model.
Finite population correction factor
A term in the formula for the variance of a hypergeometric random variable.
A function used in the probability density function of a gamma random variable that can be considered to extend factorials
A function that is used to determine properties of the probability distribution of a random variable. See Moment-generating function