 6.1: The waking times (in minutes past 5:00 a.m.) of 40 people who start...
 6.2: . The driving distances (in miles) to work of 30 people are shown b...
 6.3: Construct a 90% confidence interval for the population mean in Exer...
 6.4: Construct a 95% confidence interval for the population mean in Exer...
 6.5: (20.75, 24.10)
 6.6: . (7.428, 7.562)
 6.7: Determine the minimum sample size required to be 95% confident that...
 6.8: Determine the minimum sample size required to be 99% confident that...
 6.9: In Exercises 9 12, find the critical value tc for the level of conf...
 6.10: In Exercises 9 12, find the critical value tc for the level of conf...
 6.11: In Exercises 9 12, find the critical value tc for the level of conf...
 6.12: In Exercises 9 12, find the critical value tc for the level of conf...
 6.13: In Exercises 1316, find the margin of error for m.c = 0.90, s = 25....
 6.14: In Exercises 1316, find the margin of error for m.c = 0.95, s = 1.1...
 6.15: In Exercises 1316, find the margin of error for m. c = 0.98, s = 0....
 6.16: In Exercises 1316, find the margin of error for m. c = 0.99, s = 16...
 6.17: In Exercises 1720, construct the confidence interval for using the ...
 6.18: In Exercises 1720, construct the confidence interval for using the ...
 6.19: In Exercises 1720, construct the confidence interval for using the ...
 6.20: In Exercises 1720, construct the confidence interval for using the ...
 6.21: In a random sample of 28 sports cars, the average annual fuel cost ...
 6.22: Repeat Exercise 21 using a 99% confidence interval.
 6.23: In Exercises 2326, let p be the population proportion for the situa...
 6.24: In Exercises 2326, let p be the population proportion for the situa...
 6.25: In Exercises 2326, let p be the population proportion for the situa...
 6.26: In Exercises 2326, let p be the population proportion for the situa...
 6.27: In Exercises 2730, construct the indicated confidence interval for ...
 6.28: In Exercises 2730, construct the indicated confidence interval for ...
 6.29: In Exercises 2730, construct the indicated confidence interval for ...
 6.30: In Exercises 2730, construct the indicated confidence interval for ...
 6.31: You wish to estimate, with 95% confidence, the population proportio...
 6.32: Repeat Exercise 31 part (b), using a 99% confidence level and a mar...
 6.33: In Exercises 3336, find the critical values x2 R and x2 L for the l...
 6.34: In Exercises 3336, find the critical values x2 R and x2 L for the l...
 6.35: In Exercises 3336, find the critical values x2 R and x2 L for the l...
 6.36: In Exercises 3336, find the critical values x2 R and x2 L for the l...
 6.37: In Exercises 37 and 38, assume the sample is from a normally distri...
 6.38: In Exercises 37 and 38, assume the sample is from a normally distri...
Solutions for Chapter 6: CONFIDENCE INTERVALS
Full solutions for Elementary Statistics: Picturing the World  6th Edition
ISBN: 9780321911216
Solutions for Chapter 6: CONFIDENCE INTERVALS
Get Full SolutionsThis textbook survival guide was created for the textbook: Elementary Statistics: Picturing the World , edition: 6. Since 38 problems in chapter 6: CONFIDENCE INTERVALS have been answered, more than 99985 students have viewed full stepbystep solutions from this chapter. This expansive textbook survival guide covers the following chapters and their solutions. Elementary Statistics: Picturing the World was written by and is associated to the ISBN: 9780321911216. Chapter 6: CONFIDENCE INTERVALS includes 38 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).

Addition rule
A formula used to determine the probability of the union of two (or more) events from the probabilities of the events and their intersection(s).

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

Assignable cause
The portion of the variability in a set of observations that can be traced to speciic causes, such as operators, materials, or equipment. Also called a special cause.

Average run length, or ARL
The average number of samples taken in a process monitoring or inspection scheme until the scheme signals that the process is operating at a level different from the level in which it began.

Axioms of probability
A set of rules that probabilities deined on a sample space must follow. See Probability

Consistent estimator
An estimator that converges in probability to the true value of the estimated parameter as the sample size increases.

Correlation
In the most general usage, a measure of the interdependence among data. The concept may include more than two variables. The term is most commonly used in a narrow sense to express the relationship between quantitative variables or ranks.

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

Covariance
A measure of association between two random variables obtained as the expected value of the product of the two random variables around their means; that is, Cov(X Y, ) [( )( )] =? ? E X Y ? ? X Y .

Covariance matrix
A square matrix that contains the variances and covariances among a set of random variables, say, X1 , X X 2 k , , … . The main diagonal elements of the matrix are the variances of the random variables and the offdiagonal elements are the covariances between Xi and Xj . Also called the variancecovariance matrix. When the random variables are standardized to have unit variances, the covariance matrix becomes the correlation matrix.

Discrete uniform random variable
A discrete random variable with a inite range and constant probability mass function.

Dispersion
The amount of variability exhibited by data

Error variance
The variance of an error term or component in a model.

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

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

Fraction defective control chart
See P chart

Generating function
A function that is used to determine properties of the probability distribution of a random variable. See Momentgenerating function

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

Hat matrix.
In multiple regression, the matrix H XXX X = ( ) ? ? 1 . This a projection matrix that maps the vector of observed response values into a vector of itted values by yˆ = = X X X X y Hy ( ) ? ? ?1 .