- 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
An equation for a conditional probability such as PA B ( | ) in terms of the reverse conditional probability PB A ( | ).
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.
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.
Conditional probability density function
The probability density function of the conditional probability distribution of a continuous random variable.
A probability distribution for a continuous random variable.
A two-dimensional graphic used for a bivariate probability density function that displays curves for which the probability density function is constant.
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.
A measure of association between two random variables obtained as the expected value of the product of the two random variables around their means; that is, Cov(X Y, ) [( )( )] =? ? E X Y ? ? X Y .
A matrix that provides the tests that are to be conducted in an experiment.
Discrete random variable
A random variable with a inite (or countably ininite) range.
Error mean square
The error sum of squares divided by its number of degrees of freedom.
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.
Estimate (or point estimate)
The numerical value of a point estimator.
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.
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.
A method of variable selection in regression, where variables are inserted one at a time into the model until no other variables that contribute signiicantly to the model can be found.
The geometric mean of a set of n positive data values is the nth root of the product of the data values; that is, g x i n i n = ( ) = / w 1 1 .
Geometric random variable
A discrete random variable that is the number of Bernoulli trials until a success occurs.