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Solutions for Chapter 3.1: Elementary Statistics: Picturing the World 5th Edition

Elementary Statistics: Picturing the World | 5th Edition | ISBN: 9780321693624 | Authors: Ron Larson

Full solutions for Elementary Statistics: Picturing the World | 5th Edition

ISBN: 9780321693624

Elementary Statistics: Picturing the World | 5th Edition | ISBN: 9780321693624 | Authors: Ron Larson

Solutions for Chapter 3.1

Solutions for Chapter 3.1
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Textbook: Elementary Statistics: Picturing the World
Edition: 5
Author: Ron Larson
ISBN: 9780321693624

Since 66 problems in chapter 3.1 have been answered, more than 12318 students have viewed full step-by-step solutions from this chapter. This textbook survival guide was created for the textbook: Elementary Statistics: Picturing the World, edition: 5. This expansive textbook survival guide covers the following chapters and their solutions. Chapter 3.1 includes 66 full step-by-step solutions. Elementary Statistics: Picturing the World was written by and is associated to the ISBN: 9780321693624.

Key Statistics Terms and definitions covered in this textbook
  • 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

  • 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.

  • Arithmetic mean

    The arithmetic mean of a set of numbers x1 , x2 ,…, xn is their sum divided by the number of observations, or ( / )1 1 n xi t n ? = . The arithmetic mean is usually denoted by x , and is often called the average

  • Average run length, or ARL

    The average number of samples taken in a process monitoring or inspection scheme until the scheme signals that the process is operating at a level different from the level in which it began.

  • Bivariate distribution

    The joint probability distribution of two random variables.

  • Continuous random variable.

    A random variable with an interval (either inite or ininite) of real numbers for its range.

  • 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.

  • Correlation matrix

    A square matrix that contains the correlations among a set of random variables, say, XX X 1 2 k , ,…, . The main diagonal elements of the matrix are unity and the off-diagonal elements rij are the correlations between Xi and Xj .

  • Curvilinear regression

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

  • Decision interval

    A parameter in a tabular CUSUM algorithm that is determined from a trade-off between false alarms and the detection of assignable causes.

  • Defects-per-unit control chart

    See U chart

  • Deming’s 14 points.

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

  • Discrete random variable

    A random variable with a inite (or countably ininite) range.

  • Eficiency

    A concept in parameter estimation that uses the variances of different estimators; essentially, an estimator is more eficient than another estimator if it has smaller variance. When estimators are biased, the concept requires modiication.

  • Error mean square

    The error sum of squares divided by its number of degrees of freedom.

  • 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.

  • 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.

  • Geometric random variable

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

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