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# Solutions for Chapter 10.2.2 : Maximum-Likelihood Estimation

## Full solutions for Probability and Statistics with Reliability, Queuing, and Computer Science Applications | 2nd Edition

ISBN: 9781119285427

Solutions for Chapter 10.2.2 : Maximum-Likelihood Estimation

Solutions for Chapter 10.2.2
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##### ISBN: 9781119285427

Since 4 problems in chapter 10.2.2 : Maximum-Likelihood Estimation have been answered, more than 3291 students have viewed full step-by-step solutions from this chapter. Chapter 10.2.2 : Maximum-Likelihood Estimation includes 4 full step-by-step solutions. Probability and Statistics with Reliability, Queuing, and Computer Science Applications was written by and is associated to the ISBN: 9781119285427. This textbook survival guide was created for the textbook: Probability and Statistics with Reliability, Queuing, and Computer Science Applications , edition: 2. This expansive textbook survival guide covers the following chapters and their solutions.

Key Statistics Terms and definitions covered in this textbook
• 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

• Axioms of probability

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

• Box plot (or box and whisker plot)

A graphical display of data in which the box contains the middle 50% of the data (the interquartile range) with the median dividing it, and the whiskers extend to the smallest and largest values (or some deined lower and upper limits).

• C chart

An attribute control chart that plots the total number of defects per unit in a subgroup. Similar to a defects-per-unit or U chart.

• Central tendency

The tendency of data to cluster around some value. Central tendency is usually expressed by a measure of location such as the mean, median, or mode.

• Chance cause

The portion of the variability in a set of observations that is due to only random forces and which cannot be traced to speciic sources, such as operators, materials, or equipment. Also called a common cause.

• Conditional mean

The mean of the conditional probability distribution of a 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

• 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.

• Counting techniques

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

• Deining relation

A subset of effects in a fractional factorial design that deine the aliases in the design.

• Discrete random variable

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

• Discrete uniform random variable

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

• Dispersion

The amount of variability exhibited by data

• Eficiency

A concept in parameter estimation that uses the variances of different estimators; essentially, an estimator is more eficient than another estimator if it has smaller variance. When estimators are biased, the concept requires modiication.

• Error propagation

An analysis of how the variance of the random variable that represents that output of a system depends on the variances of the inputs. A formula exists when the output is a linear function of the inputs and the formula is simpliied if the inputs are assumed to be independent.

• Estimate (or point estimate)

The numerical value of a point estimator.

• 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.

• 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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