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Solutions for Chapter 1: Introduction to Statistics

Essentials of Statistics | 5th Edition | ISBN: 9780321924599 | Authors: Mario F. Triola

Full solutions for Essentials of Statistics | 5th Edition

ISBN: 9780321924599

Essentials of Statistics | 5th Edition | ISBN: 9780321924599 | Authors: Mario F. Triola

Solutions for Chapter 1: Introduction to Statistics

This expansive textbook survival guide covers the following chapters and their solutions. Since 12 problems in chapter 1: Introduction to Statistics have been answered, more than 14493 students have viewed full step-by-step solutions from this chapter. Essentials of Statistics was written by and is associated to the ISBN: 9780321924599. This textbook survival guide was created for the textbook: Essentials of Statistics, edition: 5. Chapter 1: Introduction to Statistics includes 12 full step-by-step solutions.

Key Statistics Terms and definitions covered in this textbook
  • `-error (or `-risk)

    In hypothesis testing, an error incurred by rejecting a null hypothesis when it is actually true (also called a type I error).

  • a-error (or a-risk)

    In hypothesis testing, an error incurred by failing to reject a null hypothesis when it is actually false (also called a type II error).

  • Additivity property of x 2

    If two independent random variables X1 and X2 are distributed as chi-square with v1 and v2 degrees of freedom, respectively, Y = + X X 1 2 is a chi-square random variable with u = + v v 1 2 degrees of freedom. This generalizes to any number of independent chi-square random variables.

  • 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

  • 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.

  • Causal variable

    When y fx = ( ) and y is considered to be caused by x, x is sometimes called a causal variable

  • Chi-square test

    Any test of signiicance based on the chi-square distribution. The most common chi-square tests are (1) testing hypotheses about the variance or standard deviation of a normal distribution and (2) testing goodness of it of a theoretical distribution to sample data

  • Conditional probability

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

  • Conidence coeficient

    The probability 1?a associated with a conidence interval expressing the probability that the stated interval will contain the true parameter value.

  • Consistent estimator

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

  • Continuous distribution

    A probability distribution for a continuous random variable.

  • Continuous uniform random variable

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

  • Control limits

    See Control chart.

  • Correction factor

    A term used for the quantity ( / )( ) 1 1 2 n xi i n ? = that is subtracted from xi i n 2 ? =1 to give the corrected sum of squares deined as (/ ) ( ) 1 1 2 n xx i x i n ? = i ? . The correction factor can also be written as nx 2 .

  • Curvilinear regression

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

  • Decision interval

    A parameter in a tabular CUSUM algorithm that is determined from a trade-off between false alarms and the detection of assignable causes.

  • Expected value

    The expected value of a random variable X is its long-term average or mean value. In the continuous case, the expected value of X is E X xf x dx ( ) = ?? ( ) ? ? where f ( ) x is the density function of the random variable X.

  • Exponential random variable

    A series of tests in which changes are made to the system under study

  • 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.

  • Gamma function

    A function used in the probability density function of a gamma random variable that can be considered to extend factorials

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