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# Solutions for Chapter CHAPTER 6 : PERCENTS AND THEIR APPLICATIONS IN BUSINESS

## Full solutions for Contemporary Mathematics | 6th Edition

ISBN: 9780538481267

Solutions for Chapter CHAPTER 6 : PERCENTS AND THEIR APPLICATIONS IN BUSINESS

Solutions for Chapter CHAPTER 6
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##### ISBN: 9780538481267

This textbook survival guide was created for the textbook: Contemporary Mathematics, edition: 6. Contemporary Mathematics was written by and is associated to the ISBN: 9780538481267. Chapter CHAPTER 6 : PERCENTS AND THEIR APPLICATIONS IN BUSINESS includes 50 full step-by-step solutions. This expansive textbook survival guide covers the following chapters and their solutions. Since 50 problems in chapter CHAPTER 6 : PERCENTS AND THEIR APPLICATIONS IN BUSINESS have been answered, more than 6160 students have viewed full step-by-step solutions from this chapter.

Key Statistics Terms and definitions covered in this textbook
• 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.

• 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.

• Attribute

A qualitative characteristic of an item or unit, usually arising in quality control. For example, classifying production units as defective or nondefective results in attributes data.

• Attribute control chart

Any control chart for a discrete random variable. See Variables control chart.

• Average run length, or ARL

The average number of samples taken in a process monitoring or inspection scheme until the scheme signals that the process is operating at a level different from the level in which it began.

• Axioms of probability

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

• Center line

A horizontal line on a control chart at the value that estimates the mean of the statistic plotted on the chart. See Control chart.

• Conditional mean

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

• Conditional probability density function

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

• Continuous distribution

A probability distribution for a continuous random variable.

• 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.

• 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 .

• Cumulative sum control chart (CUSUM)

A control chart in which the point plotted at time t is the sum of the measured deviations from target for all statistics up to time t

• Defect

Used in statistical quality control, a defect is a particular type of nonconformance to speciications or requirements. Sometimes defects are classiied into types, such as appearance defects and functional defects.

• Density function

Another name for a probability density function

• Designed experiment

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

• Erlang random variable

A continuous random variable that is the sum of a ixed number of independent, exponential random variables.

• Experiment

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

• Generating function

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

• Geometric mean.

The geometric mean of a set of n positive data values is the nth root of the product of the data values; that is, g x i n i n = ( ) = / w 1 1 .