- 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 factorial experiment.
A full factorial experiment with k factors and 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).
A formula used to determine the probability of the union of two (or more) events from the probabilities of the events and their intersection(s).
In statistical hypothesis testing, this is a hypothesis other than the one that is being tested. The alternative hypothesis contains feasible conditions, whereas the null hypothesis speciies conditions that are under test
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
See Arithmetic mean.
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
An equation for a conditional probability such as PA B ( | ) in terms of the reverse conditional probability PB A ( | ).
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.
Chi-square (or chi-squared) random variable
A continuous random variable that results from the sum of squares of independent standard normal random variables. It is a special case of a gamma random variable.
A subset selected without replacement from a set used to determine the number of outcomes in events and sample spaces.
A two-dimensional graphic used for a bivariate probability density function that displays curves for which the probability density function is constant.
See Control chart.
Discrete random variable
A random variable with a inite (or countably ininite) range.
The amount of variability exhibited by data
Error of estimation
The difference between an estimated value and the true value.
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.
Fraction defective control chart
See P chart
Effects in a fractional factorial experiment that are used to construct the experimental tests used in the experiment. The generators also deine the aliases.
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 .