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First Course in Probability 8th Edition - Solutions by Chapter

First Course in Probability | 8th Edition | ISBN: 9780136033134 | Authors: Norman S. Nise

Full solutions for First Course in Probability | 8th Edition

ISBN: 9780136033134

First Course in Probability | 8th Edition | ISBN: 9780136033134 | Authors: Norman S. Nise

First Course in Probability | 8th Edition - Solutions by Chapter

Solutions by Chapter
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This textbook survival guide was created for the textbook: First Course in Probability, edition: 8. Since problems from 10 chapters in First Course in Probability have been answered, more than 2496 students have viewed full step-by-step answer. The full step-by-step solution to problem in First Course in Probability were answered by Sieva Kozinsky, our top Statistics solution expert on 11/23/17, 05:06AM. This expansive textbook survival guide covers the following chapters: 10. First Course in Probability was written by Sieva Kozinsky and is associated to the ISBN: 9780136033134.

Key Statistics Terms and definitions covered in this textbook
  • 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

  • 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.

  • Attribute

    A qualitative characteristic of an item or unit, usually arising in quality control. For example, classifying production units as defective or nondefective results in attributes data.

  • Bivariate normal distribution

    The joint distribution of two normal random variables

  • Categorical data

    Data consisting of counts or observations that can be classiied into categories. The categories may be descriptive.

  • Causal variable

    When y fx = ( ) and y is considered to be caused by x, x is sometimes called a causal variable

  • 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.

  • Chance cause

    The portion of the variability in a set of observations that is due to only random forces and which cannot be traced to speciic sources, such as operators, materials, or equipment. Also called a common cause.

  • Conditional probability

    The probability of an event given that the random experiment produces an outcome in another event.

  • Conditional probability mass function

    The probability mass function of the conditional probability distribution of a discrete random variable.

  • Deming’s 14 points.

    A management philosophy promoted by W. Edwards Deming that emphasizes the importance of change and quality

  • 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.

  • Error variance

    The variance of an error term or component in a model.

  • Estimate (or point estimate)

    The numerical value of a point estimator.

  • Exhaustive

    A property of a collection of events that indicates that their union equals the sample space.

  • Finite population correction factor

    A term in the formula for the variance of a hypergeometric random variable.

  • 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.

  • Gamma function

    A function used in the probability density function of a gamma random variable that can be considered to extend factorials

  • Generator

    Effects in a fractional factorial experiment that are used to construct the experimental tests used in the experiment. The generators also deine the aliases.

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