×
Get Full Access to Statistics - Textbook Survival Guide
Get Full Access to Statistics - Textbook Survival Guide
×

# Solutions for Chapter 7.2: Elementary Statistics: A Step By Step Approach 9th Edition

## Full solutions for Elementary Statistics: A Step By Step Approach | 9th Edition

ISBN: 9780073534985

Solutions for Chapter 7.2

Solutions for Chapter 7.2
4 5 0 276 Reviews
14
0
##### ISBN: 9780073534985

Elementary Statistics: A Step By Step Approach was written by and is associated to the ISBN: 9780073534985. This expansive textbook survival guide covers the following chapters and their solutions. This textbook survival guide was created for the textbook: Elementary Statistics: A Step By Step Approach , edition: 9. Since 14 problems in chapter 7.2 have been answered, more than 149912 students have viewed full step-by-step solutions from this chapter. Chapter 7.2 includes 14 full step-by-step solutions.

Key Statistics Terms and definitions covered in this textbook
• 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.

• Average

See Arithmetic mean.

• 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.

• Completely randomized design (or experiment)

A type of experimental design in which the treatments or design factors are assigned to the experimental units in a random manner. In designed experiments, a completely randomized design results from running all of the treatment combinations in random order.

• Conidence coeficient

The probability 1?a associated with a conidence interval expressing the probability that the stated interval will contain the true parameter value.

• Conidence level

Another term for the conidence coeficient.

• Control limits

See Control chart.

• Counting techniques

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

• Critical value(s)

The value of a statistic corresponding to a stated signiicance level as determined from the sampling distribution. For example, if PZ z PZ ( )( .) . ? =? = 0 025 . 1 96 0 025, then z0 025 . = 1 9. 6 is the critical value of z at the 0.025 level of signiicance. Crossed factors. Another name for factors that are arranged in a factorial experiment.

• Curvilinear regression

An expression sometimes used for nonlinear regression models or polynomial regression models.

• Empirical model

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

• Error mean square

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

• 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.

• 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.

• Event

A subset of a sample space.

• 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 control chart

See P chart

• 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.

• Harmonic mean

The harmonic mean of a set of data values is the reciprocal of the arithmetic mean of the reciprocals of the data values; that is, h n x i n i = ? ? ? ? ? = ? ? 1 1 1 1 g .

×