Solutions for Chapter 12: Nonparametric Hypothesis Tests Full solutions for Introduction to Probability and Statistics for Engineers and Scientists | 5th Edition

ISBN: 9780123948113 Solutions for Chapter 12: Nonparametric Hypothesis Tests

Solutions for Chapter 12
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ISBN: 9780123948113

Since 22 problems in chapter 12: Nonparametric Hypothesis Tests have been answered, more than 3058 students have viewed full step-by-step solutions from this chapter. Chapter 12: Nonparametric Hypothesis Tests includes 22 full step-by-step solutions. Introduction to Probability and Statistics for Engineers and Scientists was written by Patricia and is associated to the ISBN: 9780123948113. This textbook survival guide was created for the textbook: Introduction to Probability and Statistics for Engineers and Scientists, edition: 5. This expansive textbook survival guide covers the following chapters and their solutions.

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

A fractional factorial experiment with k factors tested in a 2 ? p fraction with all factors tested at only two levels (settings) each

• 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.

• 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.

• Axioms of probability

A set of rules that probabilities deined on a sample space must follow. See Probability

• Bimodal distribution.

A distribution with two modes

• Bivariate distribution

The joint probability distribution of two random variables.

• Conditional variance.

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

• Continuous random variable.

A random variable with an interval (either inite or ininite) of real numbers for its range.

• Cumulative distribution function

For a random variable X, the function of X deined as PX x ( ) ? that is used to specify the probability distribution.

• Curvilinear regression

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

• Defect concentration diagram

A quality tool that graphically shows the location of defects on a part or in a process.

• Design matrix

A matrix that provides the tests that are to be conducted in an experiment.

• Discrete uniform random variable

A discrete random variable with a inite range and constant probability mass function.

• Distribution function

Another name for a cumulative distribution function.

• Error of estimation

The difference between an estimated value and the true value.

• Exhaustive

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

• Factorial experiment

A type of experimental design in which every level of one factor is tested in combination with every level of another factor. In general, in a factorial experiment, all possible combinations of factor levels are tested.

• 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.

• Generating function

A function that is used to determine properties of the probability distribution of a random variable. See Moment-generating function

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