 Chapter 103: INFERENCE FOR THE DIFFERENCE IN MEANS OF TWO NORMAL DISTRIBUTIONS, VARIANCES UNKNOWN
 Chapter 104: INFERENCE FOR THE DIFFERENCE IN MEANS OF TWO NORMAL DISTRIBUTIONS, VARIANCES UNKNOWN
 Chapter 105: INFERENCES ON THE VARIANCES OF TWO NORMAL POPULATIONS
 Chapter 106: INFERENCE ON TWO POPULATION PROPORTIONS
 Chapter 107: SUMMARY TABLE FOR INFERENCE PROCEDURES FOR TWO SAMPLES
 Chapter 10.2: INFERENCE FOR A DIFFERENCE IN MEANS OF TWO NORMAL DISTRIBUTIONS, VARIANCES KNOWN
 Chapter 1111: CORRELATION
 Chapter 112: SIMPLE LINEAR REGRESSION
 Chapter 115: HYPOTHESIS TESTS IN SIMPLE LINEAR REGRESSION
 Chapter 117: PREDICTION OF NEW OBSERVATIONS
 Chapter 118: ADEQUACY OF THE REGRESSION MODEL
 Chapter 121: MULTIPLE LINEAR REGRESSION MODEL
 Chapter 122: MULTIPLE LINEAR REGRESSION MODEL
 Chapter 123: CONFIDENCE INTERVALS IN MULTIPLE LINEAR REGRESSION
 Chapter 125: MODEL ADEQUACY CHECKING
 Chapter 126: ASPECTS OF MULTIPLE REGRESSION MODELING
 Chapter 132: THE COMPLETELY RANDOMIZED SINGLEFACTOR EXPERIMENT
 Chapter 134: RANDOMIZED COMPLETE BLOCK DESIGN
 Chapter 144: TWOFACTOR FACTORIAL EXPERIMENTS
 Chapter 145: GENERAL FACTORIAL EXPERIMENTS
 Chapter 147: 2k FACTORIAL DESIGNS
 Chapter 148: BLOCKING AND CONFOUNDING IN THE 2k DESIGN
 Chapter 149: FRACTIONAL REPLICATION OF THE 2k DESIGN
 Chapter 152: SIGN TEST
 Chapter 153: WILCOXON SIGNEDRANK TEST
 Chapter 154: WILCOXON RANKSUM TEST
 Chapter 155: NONPARAMETRIC METHODS IN THE ANALYSIS OF VARIANCE
 Chapter 1610: CUMULATIVE SUM CONTROL CHART
 Chapter 1612: IMPLEMENTING SPC
 Chapter 165: x AND R OR S CONTROL CHARTS
 Chapter 166: CONTROL CHARTS FOR INDIVIDUAL MEASUREMENTS
 Chapter 167: PROCESS CAPABILITY
 Chapter 168: ATTRIBUTE CONTROL CHARTS
 Chapter 169: CONTROL CHART PERFORMANCE
 Chapter 21: SAMPLE SPACES AND EVENTS
 Chapter 22: INTERPRETATIONS OF PROBABILITY
 Chapter 23: ADDITION RULES
 Chapter 24: CONDITIONAL PROBABILITY
 Chapter 25: MULTIPLICATION AND TOTAL PROBABILITY RULES
 Chapter 26: INDEPENDENCE
 Chapter 27: BAYES THEOREM
 Chapter 28: RANDOM VARIABLES
 Chapter 31: DISCRETE RANDOM VARIABLES
 Chapter 32: PROBABILITY DISTRIBUTIONS AND PROBABILITY MASS FUNCTIONS
 Chapter 33: CUMULATIVE DISTRIBUTION FUNCTIONS
 Chapter 34: MEAN AND VARIANCE OF A DISCRETE RANDOM VARIABLE
 Chapter 35: DISCRETE UNIFORM DISTRIBUTION
 Chapter 36: BINOMIAL DISTRIBUTION
 Chapter 37: GEOMETRIC AND NEGATIVE BINOMIAL DISTRIBUTIONS
 Chapter 38: HYPERGEOMETRIC DISTRIBUTION
 Chapter 39: POISSON DISTRIBUTION
 Chapter 333: THE RANDOMEFFECTS MODEL
 Chapter 410: ERLANG AND GAMMA DISTRIBUTIONS
 Chapter 411: WEIBULL DISTRIBUTION
 Chapter 412: LOGNORMAL DISTRIBUTION
 Chapter 42: PROBABILITY DISTRIBUTIONS AND PROBABILITY DENSITY FUNCTIONS
 Chapter 43: CUMULATIVE DISTRIBUTION FUNCTIONS
 Chapter 44: MEAN AND VARIANCE OF A CONTINUOUS RANDOM VARIABLE
 Chapter 45: CONTINUOUS UNIFORM DISTRIBUTION
 Chapter 46: NORMAL DISTRIBUTION
 Chapter 47: NORMAL APPROXIMATION TO THE BINOMIAL AND POISSON DISTRIBUTIONS
 Chapter 48: CONTINUITY CORRECTIONS TO IMPROVE THE APPROXIMATION
 Chapter 49: EXPONENTIAL DISTRIBUTION
 Chapter 51: TWO DISCRETE RANDOM VARIABLES
 Chapter 510: CHEBYSHEVS INEQUALITY (CD ONLY)
 Chapter 52: MULTIPLE DISCRETE RANDOM VARIABLES
 Chapter 53: TWO CONTINUOUS RANDOM VARIABLES
 Chapter 54: MULTIPLE CONTINUOUS RANDOM VARIABLES
 Chapter 55: COVARIANCE AND CORRELATION
 Chapter 56: BIVARIATE NORMAL DISTRIBUTION
 Chapter 57: LINEAR COMBINATIONS OF RANDOM VARIABLES
 Chapter 58: FUNCTIONS OF RANDOM VARIABLES (CD ONLY)
 Chapter 59: MOMENT GENERATING FUNCTIONS (CD ONLY)
 Chapter 61: DATA SUMMARY AND DISPLAY
 Chapter 63: STEMANDLEAF DIAGRAMS
 Chapter 64: FREQUENCY DISTRIBUTIONS AND HISTOGRAMS
 Chapter 65: BOX PLOTS
 Chapter 66: TIME SEQUENCE PLOTS
 Chapter 67: PROBABILITY PLOTS
 Chapter 68: MORE ABOUT PROBABILITY PLOTTING (CD ONLY)
 Chapter 72: GENERAL CONCEPTS OF POINT ESTIMATION
 Chapter 73: METHODS OF POINT ESTIMATION
 Chapter 75: SAMPLING DISTRIBUTIONS OF MEANS
 Chapter 82: CONFIDENCE INTERVAL ON THE MEAN OF A NORMAL DISTRIBUTION, VARIANCE KNOWN
 Chapter 83: CONFIDENCE INTERVAL ON THE MEAN OF A NORMAL DISTRIBUTION, VARIANCE UNKNOWN
 Chapter 84: CONFIDENCE INTERVAL ON THE VARIANCE AND STANDARD DEVIATION OF A NORMAL POPULATION
 Chapter 85: A LARGESAMPLE CONFIDENCE INTERVAL FOR A POPULATION PROPORTION
 Chapter 86: A PREDICTION INTERVAL FOR A FUTURE OBSERVATION
 Chapter 87: TOLERANCE INTERVALS FOR A NORMAL DISTRIBUTION
 Chapter 91: HYPOTHESIS TESTING
 Chapter 92: TESTS ON THE MEAN OF A NORMAL DISTRIBUTION, VARIANCE KNOWN
 Chapter 93: TESTS ON THE MEAN OF A NORMAL DISTRIBUTION, VARIANCE UNKNOWN
 Chapter 94: HYPOTHESIS TESTS ON THE VARIANCE AND STANDARD DEVIATION OF A NORMAL POPULATION
 Chapter 95: TESTS ON A POPULATION PROPORTION
 Chapter 97: 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
ISBN: 9780471204541
Applied Statistics and Probability for Engineers  3rd Edition  Solutions by Chapter
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Alias
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.

Backward elimination
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

Bimodal distribution.
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.

Consistent estimator
An estimator that converges in probability to the true value of the estimated parameter as the sample size increases.

Convolution
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.

Counting techniques
Formulas used to determine the number of elements in sample spaces and events.

Crossed factors
Another name for factors that are arranged in a factorial experiment.

Curvilinear regression
An expression sometimes used for nonlinear regression models or polynomial regression models.

Defectsperunit 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.

Dispersion
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).

Enumerative study
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.

Error propagation
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 modelitting 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.