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Solutions for Chapter 1-4: The Nature of Probability and Statistics

Elementary Statistics: A Step by Step Approach | 7th Edition | ISBN: 9780073534978 | Authors: Allan G. Bluman

Full solutions for Elementary Statistics: A Step by Step Approach | 7th Edition

ISBN: 9780073534978

Elementary Statistics: A Step by Step Approach | 7th Edition | ISBN: 9780073534978 | Authors: Allan G. Bluman

Solutions for Chapter 1-4: The Nature of Probability and Statistics

Chapter 1-4: The Nature of Probability and Statistics includes 6 full step-by-step solutions. This textbook survival guide was created for the textbook: Elementary Statistics: A Step by Step Approach, edition: 7. This expansive textbook survival guide covers the following chapters and their solutions. Since 6 problems in chapter 1-4: The Nature of Probability and Statistics have been answered, more than 32701 students have viewed full step-by-step solutions from this chapter. Elementary Statistics: A Step by Step Approach was written by and is associated to the ISBN: 9780073534978.

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

  • All possible (subsets) regressions

    A method of variable selection in regression that examines all possible subsets of the candidate regressor variables. Eficient computer algorithms have been developed for implementing all possible regressions

  • Analysis of variance (ANOVA)

    A method of decomposing the total variability in a set of observations, as measured by the sum of the squares of these observations from their average, into component sums of squares that are associated with speciic deined sources of variation

  • Box plot (or box and whisker plot)

    A graphical display of data in which the box contains the middle 50% of the data (the interquartile range) with the median dividing it, and the whiskers extend to the smallest and largest values (or some deined lower and upper limits).

  • 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

  • Combination.

    A subset selected without replacement from a set used to determine the number of outcomes in events and sample spaces.

  • Conidence interval

    If it is possible to write a probability statement of the form PL U ( ) ? ? ? ? = ?1 where L and U are functions of only the sample data and ? is a parameter, then the interval between L and U is called a conidence interval (or a 100 1( )% ? ? conidence interval). The interpretation is that a statement that the parameter ? lies in this interval will be true 100 1( )% ? ? of the times that such a statement is made

  • Contour plot

    A two-dimensional graphic used for a bivariate probability density function that displays curves for which the probability density function is constant.

  • Curvilinear regression

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

  • Deming’s 14 points.

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

  • Dependent variable

    The response variable in regression or a designed experiment.

  • Design matrix

    A matrix that provides the tests that are to be conducted in an experiment.

  • Distribution free method(s)

    Any method of inference (hypothesis testing or conidence interval construction) that does not depend on the form of the underlying distribution of the observations. Sometimes called nonparametric method(s).

  • Error of estimation

    The difference between an estimated value and the true value.

  • 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.

  • First-order model

    A model that contains only irstorder terms. For example, the irst-order response surface model in two variables is y xx = + ?? ? ? 0 11 2 2 + + . A irst-order model is also called a main effects model

  • Fisher’s least signiicant difference (LSD) method

    A series of pair-wise hypothesis tests of treatment means in an experiment to determine which means differ.

  • Gaussian distribution

    Another name for the normal distribution, based on the strong connection of Karl F. Gauss to the normal distribution; often used in physics and electrical engineering applications

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