×

×

# Solutions for Chapter 6: Descriptive Statistics

## Full solutions for Probability and Statistics for Engineers and Scientists | 4th Edition

ISBN: 9781111827045

Solutions for Chapter 6: Descriptive Statistics

Solutions for Chapter 6
4 5 0 306 Reviews
20
1
##### ISBN: 9781111827045

Since 73 problems in chapter 6: Descriptive Statistics have been answered, more than 53351 students have viewed full step-by-step solutions from this chapter. Chapter 6: Descriptive Statistics includes 73 full step-by-step solutions. This expansive textbook survival guide covers the following chapters and their solutions. This textbook survival guide was created for the textbook: Probability and Statistics for Engineers and Scientists, edition: 4. Probability and Statistics for Engineers and Scientists was written by and is associated to the ISBN: 9781111827045.

Key Statistics Terms and definitions covered in this textbook
• Chi-square (or chi-squared) random variable

A continuous random variable that results from the sum of squares of independent standard normal random variables. It is a special case of a gamma random variable.

• Comparative experiment

An experiment in which the treatments (experimental conditions) that are to be studied are included in the experiment. The data from the experiment are used to evaluate the treatments.

• 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.

• Contour plot

A two-dimensional graphic used for a bivariate probability density function that displays curves for which the probability density function is constant.

• Contrast

A linear function of treatment means with coeficients that total zero. A contrast is a summary of treatment means that is of interest in an experiment.

• Correlation matrix

A square matrix that contains the correlations among a set of random variables, say, XX X 1 2 k , ,…, . The main diagonal elements of the matrix are unity and the off-diagonal elements rij are the correlations between Xi and Xj .

• 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

• Deming

W. Edwards Deming (1900–1993) was a leader in the use of statistical quality control.

• Deming’s 14 points.

A management philosophy promoted by W. Edwards Deming that emphasizes the importance of change and quality

• Discrete uniform random variable

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

• 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.

• Empirical model

A model to relate a response to one or more regressors or factors that is developed from data obtained from the system.

• Enumerative study

A study in which a sample from a population is used to make inference to the population. See Analytic study

• First-order model

A model that contains only irstorder terms. For example, the irst-order response surface model in two variables is y xx = + ?? ? ? 0 11 2 2 + + . A irst-order model is also called a main effects model

• Forward selection

A method of variable selection in regression, where variables are inserted one at a time into the model until no other variables that contribute signiicantly to the model can be found.

• Gamma random variable

A random variable that generalizes an Erlang random variable to noninteger values of the parameter r

• Generating function

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

• Goodness of fit

In general, the agreement of a set of observed values and a set of theoretical values that depend on some hypothesis. The term is often used in itting a theoretical distribution to a set of observations.

• Hat matrix.

In multiple regression, the matrix H XXX X = ( ) ? ? -1 . This a projection matrix that maps the vector of observed response values into a vector of itted values by yˆ = = X X X X y Hy ( ) ? ? ?1 .