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Solutions for Chapter 8-2: CONFIDENCE INTERVAL ON THE MEAN OF A NORMAL DISTRIBUTION, VARIANCE KNOWN

Applied Statistics and Probability for Engineers | 3rd Edition | ISBN: 9780471204541 | Authors: Douglas C. Montgomery, George C. Runger

Full solutions for Applied Statistics and Probability for Engineers | 3rd Edition

ISBN: 9780471204541

Applied Statistics and Probability for Engineers | 3rd Edition | ISBN: 9780471204541 | Authors: Douglas C. Montgomery, George C. Runger

Solutions for Chapter 8-2: CONFIDENCE INTERVAL ON THE MEAN OF A NORMAL DISTRIBUTION, VARIANCE KNOWN

Solutions for Chapter 8-2
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Textbook: Applied Statistics and Probability for Engineers
Edition: 3
Author: Douglas C. Montgomery, George C. Runger
ISBN: 9780471204541

Chapter 8-2: CONFIDENCE INTERVAL ON THE MEAN OF A NORMAL DISTRIBUTION, VARIANCE KNOWN includes 18 full step-by-step solutions. Since 18 problems in chapter 8-2: CONFIDENCE INTERVAL ON THE MEAN OF A NORMAL DISTRIBUTION, VARIANCE KNOWN have been answered, more than 22688 students have viewed full step-by-step solutions from this chapter. This expansive textbook survival guide covers the following chapters and their solutions. This textbook survival guide was created for the textbook: Applied Statistics and Probability for Engineers , edition: 3. Applied Statistics and Probability for Engineers was written by and is associated to the ISBN: 9780471204541.

Key Statistics Terms and definitions covered in this textbook
  • `-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).

  • Alias

    In a fractional factorial experiment when certain factor effects cannot be estimated uniquely, they are said to be aliased.

  • 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

  • Assignable cause

    The portion of the variability in a set of observations that can be traced to speciic causes, such as operators, materials, or equipment. Also called a special cause.

  • 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.

  • 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 tendency

    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.

  • Conditional variance.

    The variance of the conditional probability distribution of a random variable.

  • Continuous uniform random variable

    A continuous random variable with range of a inite interval and a constant probability density function.

  • Control limits

    See Control chart.

  • Convolution

    A method to derive the probability density function of the sum of two independent random variables from an integral (or sum) of probability density (or mass) functions.

  • Defect concentration diagram

    A quality tool that graphically shows the location of defects on a part or in a process.

  • Dependent variable

    The response variable in regression or a designed experiment.

  • Estimator (or point estimator)

    A procedure for producing an estimate of a parameter of interest. An estimator is usually a function of only sample data values, and when these data values are available, it results in an estimate of the parameter of interest.

  • Exhaustive

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

  • Extra sum of squares method

    A method used in regression analysis to conduct a hypothesis test for the additional contribution of one or more variables to a model.

  • Forward selection

    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.

  • Geometric random variable

    A discrete random variable that is the number of Bernoulli trials until a success occurs.

  • Goodness of fit

    In general, the agreement of a set of observed values and a set of theoretical values that depend on some hypothesis. The term is often used in itting a theoretical distribution to a set of observations.

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