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Basic Practice of Statistics (Paper) & CDR6th EditionISBN: 9781464104343The Basic Practice of Statistics6th EditionISBN: 9781464102547Basic Practice Of Stat.-text (paper)5th EditionISBN: 9781429224246BASIC PRACTICE OF STATISTICS+LAUNCHPAD8th EditionISBN: 9781319053093BASIC PRACTICE OF STATISTICS WLAUNC7th EditionISBN: 9781319044251BASIC PRACTICE OF STATISTICS SAPLING8th EditionISBN: 9781319206543BASIC PRAC OF STATISTICS+LAUNCHPAD+REE8th EditionISBN: 9781319123680BASIC PRACTICE OF STATS-LL WSAPLINGPLU8th EditionISBN: 9781319216245SaplingPlus for The Basic Practice of Statistics (Six Month Access)8th EditionISBN: 9781319213237Loose-leaf Version For The Basic Practice Of Statistics8th EditionISBN: 9781319057930BASIC PRACTICE OF STATISTICS >C<8th EditionISBN: 9781319220280BASIC PRACTICE OF STATISTICS(REISSUE)>C8th EditionISBN: 9781319341831BASIC PRACTICE OF STATISTICS WSAPLING8th EditionISBN: 9781319387013BASIC PRACTICE OF STATS WSAPLING 12MO8th EditionISBN: 9781319058036Basic Practice of Statistics (Instructors)8th EditionISBN: 9781319057923The Basic Practice of Statistics8th EditionISBN: 9781319042578BASIC PRACTICE OF STATS.-ACCESS8th EditionISBN: 9781319057985The Basic Practice of Statistics8th EditionISBN: 9781319057916BASIC PRACT.OF STATISTICS (UPDATED) >C<8th EditionISBN: 9781319259891BASIC PRAC OF STAT. -WACESS >IP<7th EditionISBN: 9781319039240BASIC PRAC OF STAT. ACCESS7th EditionISBN: 9781319038953BASIC PRACTICE OF STAT PREM HMWK ACCESS7th EditionISBN: 9781319109059BASIC PRACTICE OF STATISTICS WACCESS7th EditionISBN: 9781319039233The Basic Practice of Statistics7th EditionISBN: 9781464142536The Basic Practice Of Statistics - Custom Edition For Etsu 7th Ed.7th EditionISBN: 9781319044237The Basic Practice of Statistics7th EditionISBN: 9781319116859LaunchPad for Moores the Basic Practice of Statistics (Twelve Month Access)7th EditionISBN: 9781464180828Bundle: Basic Practice of Statistics 7e & LaunchPad (Twelve Month Access)7th EditionISBN: 9781319019341Loose-leaf Version for The Basic Practice of Statistics 7e & LaunchPad (Twelve Month Access)7th EditionISBN: 9781319019334Loose-leaf Version For The Basic Practice Of Statistics7th EditionISBN: 9781464179907 Addition rule 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). Alias In a fractional factorial experiment when certain factor effects cannot be estimated uniquely, they are said to be aliased. Alternative hypothesis 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 Analytic study A study in which a sample from a population is used to make inference to a future population. Stability needs to be assumed. See Enumerative study Asymptotic relative eficiency (ARE) Used to compare hypothesis tests. The ARE of one test relative to another is the limiting ratio of the sample sizes necessary to obtain identical error probabilities for the two procedures. Bayes theorem An equation for a conditional probability such as PA B ( | ) in terms of the reverse conditional probability PB A ( | ). Bernoulli trials Sequences of independent trials with only two outcomes, generally called success and failure, in which the probability of success remains constant. Bivariate normal distribution The joint distribution of two normal random variables 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. Central limit theorem The simplest form of the central limit theorem states that the sum of n independently distributed random variables will tend to be normally distributed as n becomes large. It is a necessary and suficient condition that none of the variances of the individual random variables are large in comparison to their sum. There are more general forms of the central theorem that allow ininite variances and correlated random variables, and there is a multivariate version of the theorem. Conditional probability density function The probability density function of the conditional probability distribution of a continuous random variable. Contingency table. A tabular arrangement expressing the assignment of members of a data set according to two or more categories or classiication criteria Covariance 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 . Critical value(s) The value of a statistic corresponding to a stated signiicance level as determined from the sampling distribution. For example, if PZ z PZ ( )( .) . ? =? = 0 025 . 1 96 0 025, then z0 025 . = 1 9. 6 is the critical value of z at the 0.025 level of signiicance. Crossed factors. Another name for factors that are arranged in a factorial experiment. Cumulative distribution function For a random variable X, the function of X deined as PX x ( ) ? that is used to specify the probability distribution. Defect Used in statistical quality control, a defect is a particular type of nonconformance to speciications or requirements. Sometimes defects are classiied into types, such as appearance defects and functional defects. Defect concentration diagram A quality tool that graphically shows the location of defects on a part or in a process. 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. Discrete distribution A probability distribution for a discrete random variable 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.
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