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Solutions for Chapter 5.3: Small-Sample Confidence Intervals for a Population Mean

Statistics for Engineers and Scientists | 4th Edition | ISBN: 9780073401331 | Authors: William Navidi

Full solutions for Statistics for Engineers and Scientists | 4th Edition

ISBN: 9780073401331

Statistics for Engineers and Scientists | 4th Edition | ISBN: 9780073401331 | Authors: William Navidi

Solutions for Chapter 5.3: Small-Sample Confidence Intervals for a Population Mean

Solutions for Chapter 5.3
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Textbook: Statistics for Engineers and Scientists
Edition: 4
Author: William Navidi
ISBN: 9780073401331

Summary of Chapter 5.3: Small-Sample Confidence Intervals for a Population Mean

The methods described in Section 5.1 for computing confidence intervals for a population mean require that the sample size be large.

Since 17 problems in chapter 5.3: Small-Sample Confidence Intervals for a Population Mean have been answered, more than 710038 students have viewed full step-by-step solutions from this chapter. Statistics for Engineers and Scientists was written by and is associated to the ISBN: 9780073401331. Chapter 5.3: Small-Sample Confidence Intervals for a Population Mean includes 17 full step-by-step solutions. This textbook survival guide was created for the textbook: Statistics for Engineers and Scientists , edition: 4. This expansive textbook survival guide covers the following chapters and their solutions.

Key Statistics Terms and definitions covered in this textbook
  • 2 k p - factorial experiment

    A fractional factorial experiment with k factors tested in a 2 ? p fraction with all factors tested at only two levels (settings) each

  • Acceptance region

    In hypothesis testing, a region in the sample space of the test statistic such that if the test statistic falls within it, the null hypothesis cannot be rejected. This terminology is used because rejection of H0 is always a strong conclusion and acceptance of H0 is generally a weak conclusion

  • Adjusted R 2

    A variation of the R 2 statistic that compensates for the number of parameters in a regression model. Essentially, the adjustment is a penalty for increasing the number of parameters in the model. Alias. In a fractional factorial experiment when certain factor effects cannot be estimated uniquely, they are said to be aliased.

  • Average

    See Arithmetic mean.

  • Backward elimination

    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

  • 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.

  • Completely randomized design (or experiment)

    A type of experimental design in which the treatments or design factors are assigned to the experimental units in a random manner. In designed experiments, a completely randomized design results from running all of the treatment combinations in random order.

  • Conditional probability

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

  • Contingency table.

    A tabular arrangement expressing the assignment of members of a data set according to two or more categories or classiication criteria

  • Continuous uniform random variable

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

  • Crossed factors

    Another name for factors that are arranged in a factorial experiment.

  • Curvilinear regression

    An expression sometimes used for nonlinear regression models or polynomial regression models.

  • 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.

  • Exhaustive

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

  • F distribution.

    The distribution of the random variable deined as the ratio of two independent chi-square random variables, each divided by its number of degrees of freedom.

  • Factorial experiment

    A type of experimental design in which every level of one factor is tested in combination with every level of another factor. In general, in a factorial experiment, all possible combinations of factor levels are tested.

  • Finite population correction factor

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

  • 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.

  • Harmonic mean

    The harmonic mean of a set of data values is the reciprocal of the arithmetic mean of the reciprocals of the data values; that is, h n x i n i = ? ? ? ? ? = ? ? 1 1 1 1 g .