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Solutions for Chapter Chapter 13: Correlation and Linear Regression

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

Solutions for Chapter Chapter 13: Correlation and Linear Regression

Solutions for Chapter Chapter 13
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Textbook: Statistical Techniques in Business and Economics
Edition: 15
Author: Douglas Lind, William Marchal, Samuel Wathen
ISBN: 9780073401805

Chapter Chapter 13: Correlation and Linear Regression includes 64 full step-by-step solutions. This textbook survival guide was created for the textbook: Statistical Techniques in Business and Economics, edition: 15. Since 64 problems in chapter Chapter 13: Correlation and Linear Regression have been answered, more than 26281 students have viewed full step-by-step solutions from this chapter. This expansive textbook survival guide covers the following chapters and their solutions. Statistical Techniques in Business and Economics was written by and is associated to the ISBN: 9780073401805.

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

    A full factorial experiment with k factors and all factors tested at only two levels (settings) each.

  • 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

  • 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

  • Biased estimator

    Unbiased estimator.

  • 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.

  • Conditional variance.

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

  • Confounding

    When a factorial experiment is run in blocks and the blocks are too small to contain a complete replicate of the experiment, one can run a fraction of the replicate in each block, but this results in losing information on some effects. These effects are linked with or confounded with the blocks. In general, when two factors are varied such that their individual effects cannot be determined separately, their effects are said to be confounded.

  • 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

  • Correlation

    In the most general usage, a measure of the interdependence among data. The concept may include more than two variables. The term is most commonly used in a narrow sense to express the relationship between quantitative variables or ranks.

  • Critical region

    In hypothesis testing, this is the portion of the sample space of a test statistic that will lead to rejection of the null hypothesis.

  • Curvilinear regression

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

  • Deming

    W. Edwards Deming (1900–1993) was a leader in the use of statistical quality control.

  • Deming’s 14 points.

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

  • Designed experiment

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

  • Discrete random variable

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

  • 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).

  • Event

    A subset of a sample space.

  • 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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