 5.3.8E: assume all variables are binomial. (Note: If values are not found i...
 5.3.9E: assume all variables are binomial. (Note: If values are not found i...
 5.3.10E: assume all variables are binomial. (Note: If values are not found i...
 5.3.11E: assume all variables are binomial. (Note: If values are not found i...
 5.3.13E: assume all variables are binomial. (Note: If values are not found i...
 5.3.14E: assume all variables are binomial. (Note: If values are not found i...
 5.3.15E: assume all variables are binomial. (Note: If values are not found i...
 5.3.16E: assume all variables are binomial. (Note: If values are not found i...
 5.3.19E: Social Security Recipients A study found that 1 % of Social Securit...
 5.3.20E: Tossing Coins Find the mean, variance, and standard deviation for t...
 5.3.22E: Federal Government Employee Email Use It has been reported that 83...
 5.3.23E: Watching Fireworks A survey found that 21 % of Americans watch fire...
 5.3.24E: Alternate Sources of Fuel Eightyfive percent of Americans favor sp...
 5.3.25E: Survey on Bathing Pets A survey found that 25% of pet owners had th...
 5.3.26E: Survey on Answering Machine Ownership In a survey, 63% of Americans...
 5.3.27E: Poverty and the Federal Government One out of every three Americans...
 5.3.28E: Internet Purchases Thirtytwo percent of adult Internet users have ...
 5.3.31E: Survey of High School Seniors Of graduating high school seniors, 14...
 5.3.32E: Is this a binomial distribution? Explain.X0123P(X)0.0640.2880.4320.216
 5.3.33EC: Children in a Family The graph shown here represents the probabilit...
 5.3.34EC: Construct a binomial distribution graph for the number of defective...
Solutions for Chapter 5.3: 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 5.3
Get Full SolutionsElementary 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 21 problems in chapter 5.3 have been answered, more than 191861 students have viewed full stepbystep solutions from this chapter. Chapter 5.3 includes 21 full stepbystep solutions.

aerror (or arisk)
In hypothesis testing, an error incurred by failing to reject a null hypothesis when it is actually false (also called a type II error).

Arithmetic mean
The arithmetic mean of a set of numbers x1 , x2 ,…, xn is their sum divided by the number of observations, or ( / )1 1 n xi t n ? = . The arithmetic mean is usually denoted by x , and is often called the average

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.

Attribute
A qualitative characteristic of an item or unit, usually arising in quality control. For example, classifying production units as defective or nondefective results in attributes data.

Bayes’ estimator
An estimator for a parameter obtained from a Bayesian method that uses a prior distribution for the parameter along with the conditional distribution of the data given the parameter to obtain the posterior distribution of the parameter. The estimator is obtained from the posterior distribution.

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.

Combination.
A subset selected without replacement from a set used to determine the number of outcomes in events and sample spaces.

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.

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
The probability of an event given that the random experiment produces an outcome in another event.

Control limits
See Control chart.

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

Dispersion
The amount of variability exhibited by data

Estimate (or point estimate)
The numerical value of a point estimator.

Extra sum of squares method
A method used in regression analysis to conduct a hypothesis test for the additional contribution of one or more variables to a model.

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

Fisher’s least signiicant difference (LSD) method
A series of pairwise hypothesis tests of treatment means in an experiment to determine which means differ.

Fractional factorial experiment
A type of factorial experiment in which not all possible treatment combinations are run. This is usually done to reduce the size of an experiment with several factors.

Geometric mean.
The geometric mean of a set of n positive data values is the nth root of the product of the data values; that is, g x i n i n = ( ) = / w 1 1 .