- Chapter 10-3: INFERENCE FOR THE DIFFERENCE IN MEANS OF TWO NORMAL DISTRIBUTIONS, VARIANCES UNKNOWN
- Chapter 10-4: INFERENCE FOR THE DIFFERENCE IN MEANS OF TWO NORMAL DISTRIBUTIONS, VARIANCES UNKNOWN
- Chapter 10-5: INFERENCES ON THE VARIANCES OF TWO NORMAL POPULATIONS
- Chapter 10-6: INFERENCE ON TWO POPULATION PROPORTIONS
- Chapter 10-7: SUMMARY TABLE FOR INFERENCE PROCEDURES FOR TWO SAMPLES
- Chapter 10.2: INFERENCE FOR A DIFFERENCE IN MEANS OF TWO NORMAL DISTRIBUTIONS, VARIANCES KNOWN
- Chapter 11-11: CORRELATION
- Chapter 11-2: SIMPLE LINEAR REGRESSION
- Chapter 11-5: HYPOTHESIS TESTS IN SIMPLE LINEAR REGRESSION
- Chapter 11-7: PREDICTION OF NEW OBSERVATIONS
- Chapter 11-8: ADEQUACY OF THE REGRESSION MODEL
- Chapter 12-1: MULTIPLE LINEAR REGRESSION MODEL
- Chapter 12-2: MULTIPLE LINEAR REGRESSION MODEL
- Chapter 12-3: CONFIDENCE INTERVALS IN MULTIPLE LINEAR REGRESSION
- Chapter 12-5: MODEL ADEQUACY CHECKING
- Chapter 12-6: ASPECTS OF MULTIPLE REGRESSION MODELING
- Chapter 13-2: THE COMPLETELY RANDOMIZED SINGLE-FACTOR EXPERIMENT
- Chapter 13-4: RANDOMIZED COMPLETE BLOCK DESIGN
- Chapter 14-4: TWO-FACTOR FACTORIAL EXPERIMENTS
- Chapter 14-5: GENERAL FACTORIAL EXPERIMENTS
- Chapter 14-7: 2k FACTORIAL DESIGNS
- Chapter 14-8: BLOCKING AND CONFOUNDING IN THE 2k DESIGN
- Chapter 14-9: FRACTIONAL REPLICATION OF THE 2k DESIGN
- Chapter 15-2: SIGN TEST
- Chapter 15-3: WILCOXON SIGNED-RANK TEST
- Chapter 15-4: WILCOXON RANK-SUM TEST
- Chapter 15-5: NONPARAMETRIC METHODS IN THE ANALYSIS OF VARIANCE
- Chapter 16-10: CUMULATIVE SUM CONTROL CHART
- Chapter 16-12: IMPLEMENTING SPC
- Chapter 16-5: x AND R OR S CONTROL CHARTS
- Chapter 16-6: CONTROL CHARTS FOR INDIVIDUAL MEASUREMENTS
- Chapter 16-7: PROCESS CAPABILITY
- Chapter 16-8: ATTRIBUTE CONTROL CHARTS
- Chapter 16-9: CONTROL CHART PERFORMANCE
- Chapter 2-1: SAMPLE SPACES AND EVENTS
- Chapter 2-2: INTERPRETATIONS OF PROBABILITY
- Chapter 2-3: ADDITION RULES
- Chapter 2-4: CONDITIONAL PROBABILITY
- Chapter 2-5: MULTIPLICATION AND TOTAL PROBABILITY RULES
- Chapter 2-6: INDEPENDENCE
- Chapter 2-7: BAYES THEOREM
- Chapter 2-8: RANDOM VARIABLES
- Chapter 3-1: DISCRETE RANDOM VARIABLES
- Chapter 3-2: PROBABILITY DISTRIBUTIONS AND PROBABILITY MASS FUNCTIONS
- Chapter 3-3: CUMULATIVE DISTRIBUTION FUNCTIONS
- Chapter 3-4: MEAN AND VARIANCE OF A DISCRETE RANDOM VARIABLE
- Chapter 3-5: DISCRETE UNIFORM DISTRIBUTION
- Chapter 3-6: BINOMIAL DISTRIBUTION
- Chapter 3-7: GEOMETRIC AND NEGATIVE BINOMIAL DISTRIBUTIONS
- Chapter 3-8: HYPERGEOMETRIC DISTRIBUTION
- Chapter 3-9: POISSON DISTRIBUTION
- Chapter 33-3: THE RANDOM-EFFECTS MODEL
- Chapter 4-10: ERLANG AND GAMMA DISTRIBUTIONS
- Chapter 4-11: WEIBULL DISTRIBUTION
- Chapter 4-12: LOGNORMAL DISTRIBUTION
- Chapter 4-2: PROBABILITY DISTRIBUTIONS AND PROBABILITY DENSITY FUNCTIONS
- Chapter 4-3: CUMULATIVE DISTRIBUTION FUNCTIONS
- Chapter 4-4: MEAN AND VARIANCE OF A CONTINUOUS RANDOM VARIABLE
- Chapter 4-5: CONTINUOUS UNIFORM DISTRIBUTION
- Chapter 4-6: NORMAL DISTRIBUTION
- Chapter 4-7: NORMAL APPROXIMATION TO THE BINOMIAL AND POISSON DISTRIBUTIONS
- Chapter 4-8: CONTINUITY CORRECTIONS TO IMPROVE THE APPROXIMATION
- Chapter 4-9: EXPONENTIAL DISTRIBUTION
- Chapter 5-1: TWO DISCRETE RANDOM VARIABLES
- Chapter 5-10: CHEBYSHEVS INEQUALITY (CD ONLY)
- Chapter 5-2: MULTIPLE DISCRETE RANDOM VARIABLES
- Chapter 5-3: TWO CONTINUOUS RANDOM VARIABLES
- Chapter 5-4: MULTIPLE CONTINUOUS RANDOM VARIABLES
- Chapter 5-5: COVARIANCE AND CORRELATION
- Chapter 5-6: BIVARIATE NORMAL DISTRIBUTION
- Chapter 5-7: LINEAR COMBINATIONS OF RANDOM VARIABLES
- Chapter 5-8: FUNCTIONS OF RANDOM VARIABLES (CD ONLY)
- Chapter 5-9: MOMENT GENERATING FUNCTIONS (CD ONLY)
- Chapter 6-1: DATA SUMMARY AND DISPLAY
- Chapter 6-3: STEM-AND-LEAF DIAGRAMS
- Chapter 6-4: FREQUENCY DISTRIBUTIONS AND HISTOGRAMS
- Chapter 6-5: BOX PLOTS
- Chapter 6-6: TIME SEQUENCE PLOTS
- Chapter 6-7: PROBABILITY PLOTS
- Chapter 6-8: MORE ABOUT PROBABILITY PLOTTING (CD ONLY)
- Chapter 7-2: GENERAL CONCEPTS OF POINT ESTIMATION
- Chapter 7-3: METHODS OF POINT ESTIMATION
- Chapter 7-5: SAMPLING DISTRIBUTIONS OF MEANS
- Chapter 8-2: CONFIDENCE INTERVAL ON THE MEAN OF A NORMAL DISTRIBUTION, VARIANCE KNOWN
- Chapter 8-3: CONFIDENCE INTERVAL ON THE MEAN OF A NORMAL DISTRIBUTION, VARIANCE UNKNOWN
- Chapter 8-4: CONFIDENCE INTERVAL ON THE VARIANCE AND STANDARD DEVIATION OF A NORMAL POPULATION
- Chapter 8-5: A LARGE-SAMPLE CONFIDENCE INTERVAL FOR A POPULATION PROPORTION
- Chapter 8-6: A PREDICTION INTERVAL FOR A FUTURE OBSERVATION
- Chapter 8-7: TOLERANCE INTERVALS FOR A NORMAL DISTRIBUTION
- Chapter 9-1: HYPOTHESIS TESTING
- Chapter 9-2: TESTS ON THE MEAN OF A NORMAL DISTRIBUTION, VARIANCE KNOWN
- Chapter 9-3: TESTS ON THE MEAN OF A NORMAL DISTRIBUTION, VARIANCE UNKNOWN
- Chapter 9-4: HYPOTHESIS TESTS ON THE VARIANCE AND STANDARD DEVIATION OF A NORMAL POPULATION
- Chapter 9-5: TESTS ON A POPULATION PROPORTION
- Chapter 9-7: TESTING FOR GOODNESS OF FIT
Applied Statistics and Probability for Engineers 3rd Edition - Solutions by Chapter
Full solutions for Applied Statistics and Probability for Engineers | 3rd Edition
Applied Statistics and Probability for Engineers | 3rd Edition - Solutions by ChapterGet Full Solutions
2 k p - factorial experiment
A fractional factorial experiment with k factors tested in a 2 ? p fraction with all factors tested at only two levels (settings) each
`-error (or `-risk)
In hypothesis testing, an error incurred by rejecting a null hypothesis when it is actually true (also called a type I error).
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
Data consisting of counts or observations that can be classiied into categories. The categories may be descriptive.
A chart used to organize the various potential causes of a problem. Also called a ishbone diagram.
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 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.
The mean of the conditional probability distribution of a random variable.
Continuous uniform random variable
A continuous random variable with range of a inite interval and a constant probability density function.
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.
An expression sometimes used for nonlinear regression models or polynomial regression models.
W. Edwards Deming (1900–1993) was a leader in the use of statistical quality control.
Another name for a probability density function
Distribution free method(s)
Any method of inference (hypothesis testing or conidence interval construction) that does not depend on the form of the underlying distribution of the observations. Sometimes called nonparametric method(s).
A subset of a sample space.
A series of tests in which changes are made to the system under study
Fixed factor (or fixed effect).
In analysis of variance, a factor or effect is considered ixed if all the levels of interest for that factor are included in the experiment. Conclusions are then valid about this set of levels only, although when the factor is quantitative, it is customary to it a model to the data for interpolating between these levels.
Geometric random variable
A discrete random variable that is the number of Bernoulli trials until a success occurs.
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