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Solutions for Chapter Chapter 13: Simple Linear Regression and Correlation: Inferential Methods

Introduction to Statistics and Data Analysis (with CengageNOW Printed Access Card) (Available Titles CengageNOW) | 3rd Edition | ISBN: 9780495118732 | Authors: Roxy Peck, Chris Olsen, Jay L. Devore

Full solutions for Introduction to Statistics and Data Analysis (with CengageNOW Printed Access Card) (Available Titles CengageNOW) | 3rd Edition

ISBN: 9780495118732

Introduction to Statistics and Data Analysis (with CengageNOW Printed Access Card) (Available Titles CengageNOW) | 3rd Edition | ISBN: 9780495118732 | Authors: Roxy Peck, Chris Olsen, Jay L. Devore

Solutions for Chapter Chapter 13: Simple Linear Regression and Correlation: Inferential Methods

Solutions for Chapter Chapter 13
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Textbook: Introduction to Statistics and Data Analysis (with CengageNOW Printed Access Card) (Available Titles CengageNOW)
Edition: 3
Author: Roxy Peck, Chris Olsen, Jay L. Devore
ISBN: 9780495118732

This expansive textbook survival guide covers the following chapters and their solutions. Introduction to Statistics and Data Analysis (with CengageNOW Printed Access Card) (Available Titles CengageNOW) was written by and is associated to the ISBN: 9780495118732. Since 83 problems in chapter Chapter 13: Simple Linear Regression and Correlation: Inferential Methods have been answered, more than 18089 students have viewed full step-by-step solutions from this chapter. This textbook survival guide was created for the textbook: Introduction to Statistics and Data Analysis (with CengageNOW Printed Access Card) (Available Titles CengageNOW), edition: 3. Chapter Chapter 13: Simple Linear Regression and Correlation: Inferential Methods includes 83 full step-by-step solutions.

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.

  • 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

  • Alternative hypothesis

    In statistical hypothesis testing, this is a hypothesis other than the one that is being tested. The alternative hypothesis contains feasible conditions, whereas the null hypothesis speciies conditions that are under test

  • 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

  • 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

  • Assignable cause

    The portion of the variability in a set of observations that can be traced to speciic causes, such as operators, materials, or equipment. Also called a special cause.

  • Bivariate distribution

    The joint probability distribution of two random variables.

  • Block

    In experimental design, a group of experimental units or material that is relatively homogeneous. The purpose of dividing experimental units into blocks is to produce an experimental design wherein variability within blocks is smaller than variability between blocks. This allows the factors of interest to be compared in an environment that has less variability than in an unblocked experiment.

  • Central composite design (CCD)

    A second-order response surface design in k variables consisting of a two-level factorial, 2k axial runs, and one or more center points. The two-level factorial portion of a CCD can be a fractional factorial design when k is large. The CCD is the most widely used design for itting a second-order model.

  • Conditional probability

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

  • Conditional probability density function

    The probability density function of the conditional probability distribution of a continuous random variable.

  • Conditional probability distribution

    The distribution of a random variable given that the random experiment produces an outcome in an event. The given event might specify values for one or more other random variables

  • Consistent estimator

    An estimator that converges in probability to the true value of the estimated parameter as the sample size increases.

  • Counting techniques

    Formulas used to determine the number of elements in sample spaces and events.

  • 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 .

  • 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.

  • Crossed factors

    Another name for factors that are arranged in a factorial experiment.

  • Finite population correction factor

    A term in the formula for the variance of a hypergeometric random variable.

  • Geometric random variable

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

  • Harmonic mean

    The harmonic mean of a set of data values is the reciprocal of the arithmetic mean of the reciprocals of the data values; that is, h n x i n i = ? ? ? ? ? = ? ? 1 1 1 1 g .

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