- 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.
An attribute control chart that plots the total number of defects per unit in a subgroup. Similar to a defects-per-unit or U chart.
A chart used to organize the various potential causes of a problem. Also called a ishbone diagram.
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 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.
A subset selected without replacement from a set used to determine the number of outcomes in events and sample spaces.
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.
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 term used for the quantity ( / )( ) 1 1 2 n xi i n ? = that is subtracted from xi i n 2 ? =1 to give the corrected sum of squares deined as (/ ) ( ) 1 1 2 n xx i x i n ? = i ? . The correction factor can also be written as nx 2 .
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.
Deming’s 14 points.
A management philosophy promoted by W. Edwards Deming that emphasizes the importance of change and quality
Another name for a cumulative distribution function.
A study in which a sample from a population is used to make inference to the population. See Analytic study
Error of estimation
The difference between an estimated value and the true value.
The expected value of a random variable X is its long-term average or mean value. In the continuous case, the expected value of X is E X xf x dx ( ) = ?? ( ) ? ? where f ( ) x is the density function of the random variable X.
The distribution of the random variable deined as the ratio of two independent chi-square random variables, each divided by its number of degrees of freedom.
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.
Fisher’s least signiicant difference (LSD) method
A series of pair-wise hypothesis tests of treatment means in an experiment to determine which means differ.
A function that is used to determine properties of the probability distribution of a random variable. See Moment-generating function
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 .
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