 64.1: Explain why a normal distribution can be used as an approximation t...
 64.2: Use the normal approximation to the binomial to find the probabilit...
 64.3: Check each binomial distribution to see whether it can be approxima...
 64.4: School Enrollment Of all 3 to 5yearold children, 56% are enrolle...
 64.5: Youth Smoking Two out of five adult smokers acquired the habit by a...
 64.6: Theater Noshows A theater owner has found that 5% of patrons do no...
 64.7: Percentage of Americans Who Have Some College Education The percent...
 64.8: Household Computers According to recent surveys, 60% of households ...
 64.9: Female Americans Who Have Completed 4 Years of College The percenta...
 64.10: Population of College Cities College students often make up a subst...
 64.11: Elementary School Teachers Women comprise 80.3% of all elementary s...
 64.12: Telephone Answering Devices Seventyeight percent of U.S. homes hav...
 64.13: Parking Lot Construction The mayor of a small town estimates that 3...
 64.14: Residences of U.S. Citizens According to the U.S. Census, 67.5% of ...
 64.15: Recall that for use of a normal distribution as an approximation to...
Solutions for Chapter 64: The Normal Distribution
Full solutions for Elementary Statistics: A Step by Step Approach  7th Edition
ISBN: 9780073534978
Solutions for Chapter 64: The Normal Distribution
Get Full SolutionsThis 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: 7. Elementary Statistics: A Step by Step Approach was written by and is associated to the ISBN: 9780073534978. Since 15 problems in chapter 64: The Normal Distribution have been answered, more than 30416 students have viewed full stepbystep solutions from this chapter. Chapter 64: The Normal Distribution includes 15 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).

All possible (subsets) regressions
A method of variable selection in regression that examines all possible subsets of the candidate regressor variables. Eficient computer algorithms have been developed for implementing all possible regressions

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.

Average
See Arithmetic mean.

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

Central composite design (CCD)
A secondorder response surface design in k variables consisting of a twolevel factorial, 2k axial runs, and one or more center points. The twolevel factorial portion of a CCD can be a fractional factorial design when k is large. The CCD is the most widely used design for itting a secondorder model.

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.

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

Control chart
A graphical display used to monitor a process. It usually consists of a horizontal center line corresponding to the incontrol value of the parameter that is being monitored and lower and upper control limits. The control limits are determined by statistical criteria and are not arbitrary, nor are they related to speciication limits. If sample points fall within the control limits, the process is said to be incontrol, or free from assignable causes. Points beyond the control limits indicate an outofcontrol process; that is, assignable causes are likely present. This signals the need to ind and remove the assignable causes.

Crossed factors
Another name for factors that are arranged in a factorial experiment.

Defect
Used in statistical quality control, a defect is a particular type of nonconformance to speciications or requirements. Sometimes defects are classiied into types, such as appearance defects and functional defects.

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

Design matrix
A matrix that provides the tests that are to be conducted in an experiment.

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.

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.

Ftest
Any test of signiicance involving the F distribution. The most common Ftests are (1) testing hypotheses about the variances or standard deviations of two independent normal distributions, (2) testing hypotheses about treatment means or variance components in the analysis of variance, and (3) testing signiicance of regression or tests on subsets of parameters in a regression model.

Factorial experiment
A type of experimental design in which every level of one factor is tested in combination with every level of another factor. In general, in a factorial experiment, all possible combinations of factor levels are tested.

Geometric random variable
A discrete random variable that is the number of Bernoulli trials until a success occurs.