Solutions for Pearson eText for Probability and Statistical Inference -- Instant Access (Pearson+)
Problem 1E:
Let A and B be independent events with P(A)=0.7 and P(B)=0.2. Compute (a) P(AB), (b) P(AB), and (c)...Problem 2E:
Let P(A)=0.3 and P(B)=0.6. (a) Find P(AB) when A and B are independent. (b) Find P(AB) when A and B...Problem 3E:
Let A and B be independent events with P(A)=14 and P(B)=23. Compute (a) P(AB), (b) P(AB), (c) P(AB),...Problem 4E:
Prove parts (b) and (c) of Theorem 1.4-1.Problem 6E:
Show that if A, B, and C are mutually independent, then the following pairs of events are...Problem 7E:
Each of three football players will attempt to kick a field goal from the 25-yard line. Let Ai...Problem 8E:
Die A has orange on one face and blue on five faces. Die B has orange on two faces and blue on four...Problem 9E:
Suppose that A, B, and C are mutually independent events and that P(A)=0.5,P(B)=0.8, and P(C)=0.9....Problem 11E:
Let A and B be two events. (a) If the events A and B are mutually exclusive, are A and B always...Problem 12E:
Flip an unbiased coin five independent times. Compute the probability of (a) HHTHT. (b) THHHT. (c)...Problem 13E:
An urn contains two red balls and four white balls. Sample successively five times at random and...Problem 14E:
In Example 1.4-5, suppose that the probability of failure of a component is p=0.4. Find the...Problem 15E:
An urn contains ten red and ten white balls. The balls are drawn from the urn at random, one at a...Problem 16E:
An urn contains five balls, one marked WIN and four marked LOSE. You and another player take turns...Problem 17E:
Each of the 12 students in a class is given a fair 12-sided die. In addition, each student is...Problem 18E:
An eight-team single-elimination tournament is set up as follows: For example, eight students...Problem 19E:
Extend Example 1.4-6 to an n-sided die. That is, suppose that a fair n-sided die is rolled ii...Browse All Chapters of This Textbook
Chapter 1.1 - Properties Of ProbabilityChapter 1.2 - Methods Of EnumerationChapter 1.3 - Conditional ProbabilityChapter 1.4 - Independent EventsChapter 1.5 - Bayes’ TheoremChapter 2.1 - Random Variables Of The Discrete TypeChapter 2.2 - Mathematical ExpectationChapter 2.3 - Special Mathematical ExpectationsChapter 2.4 - The Binomial DistributionChapter 2.5 - The Hypergeometric Distribution
Chapter 2.6 - The Negative Binomial DistributionChapter 2.7 - The Poisson DistributionChapter 3.1 - Random Variables Of The Continuous TypeChapter 3.2 - The Exponential, Gamma, And Chi-square DistributionsChapter 3.3 - The Normal DistributionChapter 3.4 - Additional ModelsChapter 4.1 - Bivariate Distributions Of The Discrete TypeChapter 4.2 - The Correlation CoefficientChapter 4.3 - Conditional DistributionsChapter 4.4 - Bivariate Distributions Of The ContinuoustypeChapter 4.5 - The Bivariate Normal DistributionChapter 5.1 - Functions Of One Random VariableChapter 5.2 - Transformations Of Two Random VariablesChapter 5.3 - Several Independent Random VariablesChapter 5.4 - The Moment-generating Function TechniqueChapter 5.5 - Random Functions Associated With Normal DistributionsChapter 5.6 - The Central Limit TheoremChapter 5.7 - Approximations For Discrete DistributionsChapter 5.8 - Chebyshev’s Inequality And Convergence In ProbabilityChapter 5.9 - Limiting Moment-generating FunctionsChapter 7.2 - Confidence Intervals For The Difference Of Two MeansChapter 8.1 - Tests About One Mean
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Probability and Statistical Inference
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