 Chapter 1: Picturing Distributions with Graphs
 Chapter 10: Introducing Probability
 Chapter 11: Sampling Distributions
 Chapter 12: General Rules of Probability
 Chapter 13: Binomial Distributions
 Chapter 14: Confidence Intervals: The Basics
 Chapter 15: Tests of Significance: The Basics
 Chapter 16: Inference in Practice
 Chapter 17: From Exploration to Inference: Part II Review
 Chapter 18: Inference about a Population Mean
 Chapter 19: TwoSample Problems
 Chapter 2: Describing Distributions with Numbers
 Chapter 20: Inference about a Population Proportion
 Chapter 21: Comparing Two Proportions
 Chapter 22: Inference about Variables: Part III Review
 Chapter 23: Two Categorical Variables: The ChiSquare Test
 Chapter 24: Inference for Regression
 Chapter 25: OneWay Analysis of Variance: Comparing Several Means
 Chapter 26: Nonparametric Tests
 Chapter 27: Statistical Process Control
 Chapter 28: Multiple Regression
 Chapter 3: The Normal Distributions
 Chapter 4: Scatterplots and Correlation
 Chapter 5: Regression
 Chapter 6: TwoWay Tables
 Chapter 7: Exploring Data: Part I Review
 Chapter 8: Producing Data: Sampling
 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

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).

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.

Biased estimator
Unbiased estimator.

Box plot (or box and whisker plot)
A graphical display of data in which the box contains the middle 50% of the data (the interquartile range) with the median dividing it, and the whiskers extend to the smallest and largest values (or some deined lower and upper limits).

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.

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.

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.

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

Conidence interval
If it is possible to write a probability statement of the form PL U ( ) ? ? ? ? = ?1 where L and U are functions of only the sample data and ? is a parameter, then the interval between L and U is called a conidence interval (or a 100 1( )% ? ? conidence interval). The interpretation is that a statement that the parameter ? lies in this interval will be true 100 1( )% ? ? of the times that such a statement is made

Control limits
See Control chart.

Cumulative distribution function
For a random variable X, the function of X deined as PX x ( ) ? that is used to specify the probability distribution.

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

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.

Deining relation
A subset of effects in a fractional factorial design that deine the aliases in the design.

Designed experiment
An experiment in which the tests are planned in advance and the plans usually incorporate statistical models. See Experiment

Discrete distribution
A probability distribution for a discrete random variable

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 modelitting process and not on replication.

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.

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 .