 Chapter Chapter 1: Picturing Distributions with Graphs
 Chapter Chapter 10: Introducing Probability
 Chapter Chapter 11: Sampling Distributions
 Chapter Chapter 12: General Rules of Probability
 Chapter Chapter 13: Binomial Distributions
 Chapter Chapter 14: Confidence Intervals: The Basics
 Chapter Chapter 15: Tests of Significance: The Basics
 Chapter Chapter 16: Inference in Practice
 Chapter Chapter 17: From Exploration to Inference: Part II Review
 Chapter Chapter 18: Inference about a Population Mean
 Chapter Chapter 19: TwoSample Problems
 Chapter Chapter 2: Describing Distributions with Numbers
 Chapter Chapter 20: Inference about a Population Proportion
 Chapter Chapter 21: Comparing Two Proportions
 Chapter Chapter 22: Inference about Variables: Part III Review
 Chapter Chapter 23: Two Categorical Variables: The ChiSquare Test
 Chapter Chapter 24: Inference for Regression
 Chapter Chapter 25: OneWay Analysis of Variance: Comparing Several Means
 Chapter Chapter 26: Nonparametric Tests
 Chapter Chapter 27: Statistical Process Control
 Chapter Chapter 28: Multiple Regression
 Chapter Chapter 3: The Normal Distributions
 Chapter Chapter 4 : Scatterplots and Correlation
 Chapter Chapter 5: Regression
 Chapter Chapter 6: TwoWay Tables
 Chapter Chapter 7: Exploring Data: Part I Review
 Chapter Chapter 8: Producing Data: Sampling
 Chapter Chapter 9: Producing Data: Experiments
The Basic Practice of Statistics 4th Edition  Solutions by Chapter
Full solutions for The Basic Practice of Statistics  4th Edition
ISBN: 9780716774785
The Basic Practice of Statistics  4th Edition  Solutions by Chapter
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Acceptance region
In hypothesis testing, a region in the sample space of the test statistic such that if the test statistic falls within it, the null hypothesis cannot be rejected. This terminology is used because rejection of H0 is always a strong conclusion and acceptance of H0 is generally a weak conclusion

Alias
In a fractional factorial experiment when certain factor effects cannot be estimated uniquely, they are said to be aliased.

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

Analytic study
A study in which a sample from a population is used to make inference to a future population. Stability needs to be assumed. See Enumerative study

Bimodal distribution.
A distribution with two modes

Central limit theorem
The simplest form of the central limit theorem states that the sum of n independently distributed random variables will tend to be normally distributed as n becomes large. It is a necessary and suficient condition that none of the variances of the individual random variables are large in comparison to their sum. There are more general forms of the central theorem that allow ininite variances and correlated random variables, and there is a multivariate version of the theorem.

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.

Chisquare test
Any test of signiicance based on the chisquare distribution. The most common chisquare tests are (1) testing hypotheses about the variance or standard deviation of a normal distribution and (2) testing goodness of it of a theoretical distribution to sample data

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.

Convolution
A method to derive the probability density function of the sum of two independent random variables from an integral (or sum) of probability density (or mass) functions.

Correlation coeficient
A dimensionless measure of the linear association between two variables, usually lying in the interval from ?1 to +1, with zero indicating the absence of correlation (but not necessarily the independence of the two variables).

Deming
W. Edwards Deming (1900–1993) was a leader in the use of statistical quality control.

Dependent variable
The response variable in regression or a designed experiment.

Distribution function
Another name for a cumulative distribution function.

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.

Finite population correction factor
A term in the formula for the variance of a hypergeometric random variable.

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

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.

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 .