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Solutions for Chapter 6: The Standard Deviation as a Ruler and the Normal Model

Stats: Modeling The World | 3rd Edition | ISBN: 9780131359581 | Authors: David E. Bock

Full solutions for Stats: Modeling The World | 3rd Edition

ISBN: 9780131359581

Stats: Modeling The World | 3rd Edition | ISBN: 9780131359581 | Authors: David E. Bock

Solutions for Chapter 6: The Standard Deviation as a Ruler and the Normal Model

Solutions for Chapter 6
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Textbook: Stats: Modeling The World
Edition: 3
Author: David E. Bock
ISBN: 9780131359581

This textbook survival guide was created for the textbook: Stats: Modeling The World , edition: 3. Stats: Modeling The World was written by and is associated to the ISBN: 9780131359581. This expansive textbook survival guide covers the following chapters and their solutions. Since 47 problems in chapter 6: The Standard Deviation as a Ruler and the Normal Model have been answered, more than 27962 students have viewed full step-by-step solutions from this chapter. Chapter 6: The Standard Deviation as a Ruler and the Normal Model includes 47 full step-by-step solutions.

Key Statistics Terms and definitions covered in this textbook
  • 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

  • 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

  • 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.

  • Bimodal distribution.

    A distribution with two modes

  • 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

  • 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 mean

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

  • Conditional probability

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

  • Conidence level

    Another term for the conidence coeficient.

  • Continuity correction.

    A correction factor used to improve the approximation to binomial probabilities from a normal distribution.

  • Continuous uniform random variable

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

  • 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.

  • Degrees of freedom.

    The number of independent comparisons that can be made among the elements of a sample. The term is analogous to the number of degrees of freedom for an object in a dynamic system, which is the number of independent coordinates required to determine the motion of the object.

  • Discrete random variable

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

  • Estimator (or point estimator)

    A procedure for producing an estimate of a parameter of interest. An estimator is usually a function of only sample data values, and when these data values are available, it results in an estimate of the parameter of interest.

  • Exhaustive

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

  • Gamma random variable

    A random variable that generalizes an Erlang random variable to noninteger values of the parameter r

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