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Solutions for Chapter 11.5: PUTTING IT TOGETHER: WHICH METHOD DO I USE?

Full solutions for Statistics: Informed Decisions Using Data | 4th Edition

ISBN: 9780321757272

Solutions for Chapter 11.5: PUTTING IT TOGETHER: WHICH METHOD DO I USE?

Solutions for Chapter 11.5
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ISBN: 9780321757272

This expansive textbook survival guide covers the following chapters and their solutions. Statistics: Informed Decisions Using Data was written by and is associated to the ISBN: 9780321757272. Chapter 11.5: PUTTING IT TOGETHER: WHICH METHOD DO I USE? includes 28 full step-by-step solutions. Since 28 problems in chapter 11.5: PUTTING IT TOGETHER: WHICH METHOD DO I USE? have been answered, more than 145082 students have viewed full step-by-step solutions from this chapter. This textbook survival guide was created for the textbook: Statistics: Informed Decisions Using Data , edition: 4.

Key Statistics Terms and definitions covered in this textbook
• Analysis of variance (ANOVA)

A method of decomposing the total variability in a set of observations, as measured by the sum of the squares of these observations from their average, into component sums of squares that are associated with speciic deined sources of variation

• Attribute control chart

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

• Bayesâ€™ theorem

An equation for a conditional probability such as PA B ( | ) in terms of the reverse conditional probability PB A ( | ).

• Bimodal distribution.

A distribution with two modes

• Cause-and-effect diagram

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

• Components of variance

The individual components of the total variance that are attributable to speciic sources. This usually refers to the individual variance components arising from a random or mixed model analysis of variance.

• 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

• Conditional probability mass function

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

• Confounding

When a factorial experiment is run in blocks and the blocks are too small to contain a complete replicate of the experiment, one can run a fraction of the replicate in each block, but this results in losing information on some effects. These effects are linked with or confounded with the blocks. In general, when two factors are varied such that their individual effects cannot be determined separately, their effects are said to be confounded.

• Contingency table.

A tabular arrangement expressing the assignment of members of a data set according to two or more categories or classiication criteria

• 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.

• Defect concentration diagram

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

• Degrees of freedom.

The number of independent comparisons that can be made among the elements of a sample. The term is analogous to the number of degrees of freedom for an object in a dynamic system, which is the number of independent coordinates required to determine the motion of the object.

• 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

• Erlang random variable

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

• Error mean square

The error sum of squares divided by its number of degrees of freedom.

• Estimate (or point estimate)

The numerical value of a point estimator.

• 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

• Fraction defective

In statistical quality control, that portion of a number of units or the output of a process that is defective.

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