- 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
In a fractional factorial experiment when certain factor effects cannot be estimated uniquely, they are said to be aliased.
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.
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
A distribution with two modes
Binomial random variable
A discrete random variable that equals the number of successes in a ixed number of Bernoulli trials.
Conditional probability density function
The probability density function of the conditional probability distribution of a continuous random variable.
An estimator that converges in probability to the true value of the estimated parameter as the sample size increases.
A method to derive the probability density function of the sum of two independent random variables from an integral (or sum) of probability density (or mass) functions.
Formulas used to determine the number of elements in sample spaces and events.
Another name for factors that are arranged in a factorial experiment.
An expression sometimes used for nonlinear regression models or polynomial regression models.
Defects-per-unit control chart
See U chart
Degrees of freedom.
The number of independent comparisons that can be made among the elements of a sample. The term is analogous to the number of degrees of freedom for an object in a dynamic system, which is the number of independent coordinates required to determine the motion of the object.
The amount of variability exhibited by data
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 study in which a sample from a population is used to make inference to the population. See Analytic study
Error mean square
The error sum of squares divided by its number of degrees of freedom.
An analysis of how the variance of the random variable that represents that output of a system depends on the variances of the inputs. A formula exists when the output is a linear function of the inputs and the formula is simpliied if the inputs are assumed to be independent.
Error sum of squares
In analysis of variance, this is the portion of total variability that is due to the random component in the data. It is usually based on replication of observations at certain treatment combinations in the experiment. It is sometimes called the residual sum of squares, although this is really a better term to use only when the sum of squares is based on the remnants of a model-itting process and not on replication.
Estimator (or point estimator)
A procedure for producing an estimate of a parameter of interest. An estimator is usually a function of only sample data values, and when these data values are available, it results in an estimate of the parameter of interest.