- 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: Two-Sample 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 Chi-Square Test
- Chapter Chapter 24: Inference for Regression
- Chapter Chapter 25: One-Way 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: Two-Way 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
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
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.
An effect that systematically distorts a statistical result or estimate, preventing it from representing the true quantity of interest.
The joint probability distribution of two random variables.
In experimental design, a group of experimental units or material that is relatively homogeneous. The purpose of dividing experimental units into blocks is to produce an experimental design wherein variability within blocks is smaller than variability between blocks. This allows the factors of interest to be compared in an environment that has less variability than in an unblocked 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 distribution
The distribution of a random variable given that the random experiment produces an outcome in an event. The given event might specify values for one or more other random variables
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
A tabular arrangement expressing the assignment of members of a data set according to two or more categories or classiication criteria
See Control chart.
Formulas used to determine the number of elements in sample spaces and events.
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 off-diagonal elements are the covariances between Xi and Xj . Also called the variance-covariance matrix. When the random variables are standardized to have unit variances, the covariance matrix becomes the correlation matrix.
Another name for factors that are arranged in a factorial experiment.
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.
Estimator (or point estimator)
A procedure for producing an estimate of a parameter of interest. An estimator is usually a function of only sample data values, and when these data values are available, it results in an estimate of the parameter of interest.
A property of a collection of events that indicates that their union equals the sample space.
A signal from a control chart when no assignable causes are present
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.
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.