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Statistical Techniques in Business and Economics 15th Edition - Solutions by Chapter

Statistical Techniques in Business and Economics | 15th Edition | ISBN: 9780073401805 | Authors: Douglas Lind, William Marchal, Samuel Wathen

Full solutions for Statistical Techniques in Business and Economics | 15th Edition

ISBN: 9780073401805

Statistical Techniques in Business and Economics | 15th Edition | ISBN: 9780073401805 | Authors: Douglas Lind, William Marchal, Samuel Wathen

Statistical Techniques in Business and Economics | 15th Edition - Solutions by Chapter

The full step-by-step solution to problem in Statistical Techniques in Business and Economics were answered by Patricia, our top Statistics solution expert on 03/16/18, 04:51PM. Statistical Techniques in Business and Economics was written by Patricia and is associated to the ISBN: 9780073401805. Since problems from 20 chapters in Statistical Techniques in Business and Economics have been answered, more than 3738 students have viewed full step-by-step answer. This expansive textbook survival guide covers the following chapters: 20. This textbook survival guide was created for the textbook: Statistical Techniques in Business and Economics, edition: 15.

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

  • 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.

  • 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

  • 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).

  • 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.

  • Conditional probability mass function

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

  • Contrast

    A linear function of treatment means with coeficients that total zero. A contrast is a summary of treatment means that is of interest in an experiment.

  • 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 .

  • Covariance

    A measure of association between two random variables obtained as the expected value of the product of the two random variables around their means; that is, Cov(X Y, ) [( )( )] =? ? E X Y ? ? X Y .

  • Defects-per-unit control chart

    See U chart

  • Designed experiment

    An experiment in which the tests are planned in advance and the plans usually incorporate statistical models. See Experiment

  • Enumerative study

    A study in which a sample from a population is used to make inference to the population. See Analytic study

  • Erlang random variable

    A continuous random variable that is the sum of a ixed number of independent, exponential random variables.

  • 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.

  • 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.

  • 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

  • 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.

  • Fraction defective control chart

    See P chart

  • Fractional factorial experiment

    A type of factorial experiment in which not all possible treatment combinations are run. This is usually done to reduce the size of an experiment with several factors.

  • Frequency distribution

    An arrangement of the frequencies of observations in a sample or population according to the values that the observations take on

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