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# Solutions for Chapter 11.8: HIGHER-DIMENSIONAL LEAST-SQUARES FIT

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

ISBN: 9781119285427

Solutions for Chapter 11.8: HIGHER-DIMENSIONAL LEAST-SQUARES FIT

Solutions for Chapter 11.8
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##### ISBN: 9781119285427

This expansive textbook survival guide covers the following chapters and their solutions. Chapter 11.8: HIGHER-DIMENSIONAL LEAST-SQUARES FIT includes 1 full step-by-step solutions. This textbook survival guide was created for the textbook: Probability and Statistics with Reliability, Queuing, and Computer Science Applications , edition: 2. Since 1 problems in chapter 11.8: HIGHER-DIMENSIONAL LEAST-SQUARES FIT have been answered, more than 2947 students have viewed full step-by-step solutions from this chapter. Probability and Statistics with Reliability, Queuing, and Computer Science Applications was written by and is associated to the ISBN: 9781119285427.

Key Statistics Terms and definitions covered in this textbook

A variation of the R 2 statistic that compensates for the number of parameters in a regression model. Essentially, the adjustment is a penalty for increasing the number of parameters in the model. Alias. In a fractional factorial experiment when certain factor effects cannot be estimated uniquely, they are said to be aliased.

• 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

• Bivariate distribution

The joint probability distribution of two random variables.

• Categorical data

Data consisting of counts or observations that can be classiied into categories. The categories may be descriptive.

• Cause-and-effect diagram

A chart used to organize the various potential causes of a problem. Also called a ishbone diagram.

• 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.

• Coeficient of determination

See R 2 .

• Conditional probability density function

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

• Conditional probability mass function

The probability mass function of the conditional probability distribution of a discrete random variable.

• Continuity correction.

A correction factor used to improve the approximation to binomial probabilities from a normal distribution.

• Continuous random variable.

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

• Cook’s distance

In regression, Cook’s distance is a measure of the inluence of each individual observation on the estimates of the regression model parameters. It expresses the distance that the vector of model parameter estimates with the ith observation removed lies from the vector of model parameter estimates based on all observations. Large values of Cook’s distance indicate that the observation is inluential.

• Deming

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

• Discrete distribution

A probability distribution for a discrete random variable

• Discrete random variable

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

• Distribution function

Another name for a cumulative distribution function.

• Error sum of squares

In analysis of variance, this is the portion of total variability that is due to the random component in the data. It is usually based on replication of observations at certain treatment combinations in the experiment. It is sometimes called the residual sum of squares, although this is really a better term to use only when the sum of squares is based on the remnants of a model-itting process and not on replication.

• Experiment

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

• Frequency distribution

An arrangement of the frequencies of observations in a sample or population according to the values that the observations take on

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